Agricultural Treatment Control Device

ABSTRACT

The invention relates to a collaborative agricultural field processing control device intended to be mounted on an agricultural machine (1), composed of a set of detectors (2) for weeds or leaf symptoms of deficiencies or diseases collaborating in the decision to control the treatment devices (3) of the agricultural field.

FIELD OF THE INVENTION

The present invention relates to a device for controlling agriculturaltreatment to be mounted on an agricultural machine, integrating at leastone controllable device for treating the field and at least one detectorfor weeds or foliar symptoms of deficiencies or diseases.

TECHNOLOGICAL BACKGROUND

Agricultural crops require regular maintenance in order to optimizeagricultural production. Fertilization treatments, weed control, fightagainst deficiencies, or fight against diseases or pests are necessaryin order to optimize the production yield of these crops.

Modern farming techniques tend to reduce inputs and treatments. To thisend, they offer different methods to deal with these problems.

Prophylactic measures, the objective of which is to minimize the numberof weeds present on an agricultural field competing with the crop, arewidely used in the technical itineraries of field crops and marketgardening. The following methods are recommended for this purpose:

-   -   Crop rotation was one of the first methods theorized at the        start of the 20th century, as described in the document        “Clyde E. Leighty, 1938 Yearbook of Agriculture”, consisting of        alternating fall and spring crops in order to break certain        biological cycles of weeds;    -   plowing makes it possible to reduce the number of weeds in the        crop by burying their seeds;    -   the false seedling, as described in the document “Evaluating        Cover Crops for Benefits, Costs and Performance within Cropping        System Niches”, Agronomy Journal 97 (1). American Society of        Agronomy: 322-32, by raising rapidly emerging weeds that are        destroyed before the crop is planted also reduces the rate of        weeds present in the crop.

The recommended curative measures, the objective of which is to avoidweed rising in the crop, and to have an impact on the yield of the crop,are as follows:

-   -   Chemical weeding, as described in the document «Pulvérisation en        grandes cultures. Les clés de la réussite» Arvalis, helps        prevent crop weeds. Phytosanitary products dedicated to chemical        weed control are suitable either for a pre-emergence treatment        making it possible to avoid the germination of the weeds present        in the seed state, or for a post-emergence treatment, making it        possible to destroy the weeds which have emerged in the crop.        Chemical weeding is either selective, making it possible to        treat a weed typology, or non-selective, making it possible to        destroy all of the plants present in the plot at the time of        treatment. Repeated use of the same chemical group of weedkiller        results in the development of weed resistance, as well as        phytotoxicity which has an impact on crop yield. Chemical weed        killers are applied to the plot using a sprayer;    -   mechanical weeding as described in the document “La bineuse, un        outil adapté à une large gamme de sol”, Arvalis, in        pre-emergence or post-emergence, makes it possible to destroy        either the weed seedlings, or the weeds to a more advanced        stage. This weeding process improves the soil structure and also        disrupts the cycle of some pests. The tools used for mechanical        weeding are weeder harrows or rotary hoes for complete weeding        or hoeing machines with teeth for inter-row or under-row        treatment;    -   alternative methods are developed, as described in the document        “Alternative methods in weed management to the use of glyphosate        and other herbicide”, pesticide action network europe, 2018        (second edition), with in particular, the treatment of weeds by        injection of an electric current, consisting in destroying each        weed raised by bursting of the biological cells by causing an        evaporation of the water contained in them, the treatments of        the weeds by heat, whose methods are based on the use of laser,        or high pressure hot water, can selectively destroy weeds by        providing enough calories to destroy biological cells in the        weed.

The recommended methods for treating deficiencies and diseases or forcombating pests are essentially based on chemical treatments.

The treatments, whether chemical, mechanical or alternative, are carriedout by a machine, generally attached to a motorized vehicle that movesin the crop.

These treatments are traditionally broad and do not take into accountthe presence or absence of weeds, pests, deficiencies or diseases, bytreating the entire agricultural plot in a homogeneous manner. Thismethod of control is imprecise and leads to overuse of phytosanitaryproducts when the treatment is chemical, or a reduced work rate when thetreatment is mechanical or alternative.

In this context, the thesis “Segmentation d'images pour la localisationd'adventices. Application à la réalisation d'un système de vision pourune pulvérisation spécifique en temps réel”, Jérémie Bossu, Universityof Burgundy/CNRS 5158, Dec. 4, 2007, describes an experimental devicefor piloting spreading comprising a camera mounted on an agriculturalmachine, a central unit for detecting and calculating the optimal momentof spraying, taking into account the position of plants in the imagesacquired by the camera.

Document WO2012/032245, “Control system for agricultural spreading”,describes a spreading control system comprising a set of spreadingnozzles, means for mapping plants to be treated using, in oneembodiment, cameras, and means for controlling the spreading accordingto the cartography data produced. This control system requires a firstpass of the system in the agricultural plot in order to produce amapping of this agricultural plot used in a second pass for theapplication of the treatment.

The document WO2012/122988, “Spray bar for selectively spraying aweedkiller composition on dicotyledons”, describes a method making itpossible to distinguish a category of weeds from other weeds in order toselectively treat the weeds concerned by detection. This process uses astereoscopic camera in order to discern weeds, and does not make itpossible to discern weeds of the same family, for example dicots, atearly stages. Similarly, this process is not suitable for the detectionof leaf symptoms of deficiencies or diseases.

The document US2018/0240228, “Selective plant detection and treatmentusing green luminance photometric machine vision scan with real timechromaticity operations and image parameter floors for low processingload”, describes a method for detecting plants in an image and target itin a treatment. This process is not suitable for the selective detectionof weed families, nor for the detection of leaf symptoms of deficienciesor diseases.

The document FR 3 063 206 comprises several embodiments, but the mainembodiment comprises a single processing unit, which may use images fromseveral cameras. Although this document also mentions “severalprocessing units”, this mention is brief, and the only practicalembodiment is that of a plurality of control subsystems each comprisinga processing unit.

Document CN 108 990 944 seems to describe a drone carrying a camera inthe visible range and an infrared camera, the images of which are mergedby a central processor.

Furthermore, the document FR1873313, “Hyperspectral acquisitiondetection device” describes a hyperspectral acquisition device withdirect detection capable of detecting the shape, texture and spectralreflectance signature of a weed, or of leaf symptoms of deficiency ordisease in a crop. This device is suitable for discerning weeds in theearly stages, including weeds of the same family. Likewise, this deviceis suitable for the detection of leaf symptoms of deficiencies ordiseases. The document FR1901202, “Hyperspectral detection device byfusion of sensors”, describes an alternative direct detection methodable to detect the presence of weeds, or foliar symptoms of deficienciesor diseases in a culture image. The documents FR1905916 andWO2019EP85847 repeat and supplement the two previous documents. Theselast four patent applications are incorporated herein by reference intheir entirety for any useful purpose.

There are many problems with weed detection. The formulations of thechemical treatments for selective weeding, are addressed, for each ofthem, to a family of weeds, for example the dicotyledons, and have aneffectiveness adapted to certain stages of development of the weed, forexample the seedling stage. It therefore appears necessary to be able todiscern with great reliability the weeds of a certain family among allthe plants present in the agricultural plot. Equivalently, the detectionof diseases or deficiencies in a culture requires high detectionreliability in order to cover all of the affected areas in the culture.

In addition, agricultural treatment equipment, in particular sprayingbooms, can cover a large width, up to 50 m, of treatment; these rampsthen have a large number of treatment nozzles. A detection system musttherefore be capable of detecting with great reliability the presence ofcertain families of weeds or leaf symptoms of deficiencies or diseases,over a large width.

Thus, the technical problem of the invention consists in detecting thepresence of weeds, or foliar symptoms of deficiencies or diseases inreal time during the travel of an agricultural machine.

SUMMARY OF THE INVENTION

The present invention proposes to respond to this technical problem byequipping an agricultural machine with a set of sensors for weeds orfoliar symptoms of deficiencies or diseases; said sensors of weeds orfoliar symptoms of deficiencies or diseases collaborating in thedetection and control of the treatment to be applied according to thedetections made by each of said sensors of weeds or foliar symptoms ofdeficiencies or diseases.

To this end, the invention relates to an agricultural treatment controldevice intended to be mounted on an agricultural machine, saidagricultural machine comprising at least one controllable treatmentdevice, the agricultural treatment control device comprising:

-   -   at least one deficiency or disease foliar symptoms or weeds        detection system, each being suitable for attachment to the        agricultural machine,    -   a system for localising at least one deficiency or disease        foliar symptoms or weeds detection system.

The invention is characterized in that at least one deficiency ordisease foliar symptoms or weeds detection system collaborates with adeficiency or disease foliar symptoms or weeds detection system of whichthe detection zone partially overlaps with that of said deficiency ordisease foliar symptoms or weeds detection system in order tocollaboratively decide on the treatment to be applied to the detectionzone of said deficiency or disease foliar symptoms or weeds detection.The device comprises a communication system between said at least onedeficiency or disease foliar symptoms or weeds detection systems and atleast one treatment device. This embodiment allows selective chemical,thermal or mechanical treatment in an agricultural plot.

Within the meaning of the invention, an agricultural treatment controldevice is composed of at least one sensor detecting the presence andlocalisation of weeds or leaf symptoms of deficiencies or diseases in anagricultural plot, and a collaborative automated decision-making processfor applying a treatment; the treatment being able to be of differentnatures in particular chemical, mechanical or electrical.

According to one embodiment, said at least one deficiency or diseasefoliar symptoms or weeds detection system is adapted to collaborate withanother deficiency or disease foliar symptoms or weeds detection systemwhose detection zone laterally partially overlaps with that of saiddeficiency or disease foliar symptoms or weeds detection system.

According to one embodiment, said at least one deficiency or diseasefoliar symptoms or weeds detection system is adapted to collaborate witha deficiency or disease foliar symptoms or weeds detection system whosedetection zone temporally overlaps with that of said deficiency ordisease foliar symptoms or weeds detection system.

According to one embodiment, the localisation system comprises ageolocalisation system and/or an inertial unit.

According to one embodiment, the device comprises at least twodeficiency or disease foliar symptoms or weeds detection systems.

According to one embodiment, one, in particular each, deficiency ordisease foliar symptoms or weeds detection system is equipped with alocalisation system.

According to one embodiment, one, in particular each, deficiency ordisease foliar symptoms or weeds detection system is adapted tocollaborate with another, in particular the others, deficiency ordisease foliar symptoms or weeds detection systems.

According to one embodiment, one, in particular each, deficiency ordisease foliar symptoms or weeds detection system comprises ahyperspectral sensor.

According to one embodiment, a deficiency or disease foliar symptoms orweeds detection system is adapted to detect the presence of weeds orfoliar symptoms of deficiency or disease from peculiarities specific toweeds or leaf symptoms of deficiencies or diseases.

According to one embodiment, a deficiency or disease foliar symptoms orweeds detection detection system is adapted to detect an area for a weedor a foliar symptom of deficiency or disease.

According to one embodiment, a deficiency or disease foliar symptoms orweeds detection system is supplemented with a probability of thepresence of said characteristics specific to weeds or foliar symptoms ofdeficiencies or diseases.

According to one embodiment, the localisation system is adapted tolocalise the treatment to be applied to the detection area.

According to one embodiment, the device comprises a communication systembetween said deficiency or disease foliar symptoms or weeds detectionsystems.

According to one embodiment, a temporal overlap of said information ofdetections of weeds or leaf symptoms of deficiencies or diseases isobtained.

According to one embodiment, one, in particular each, detection systemcomprises a system for direct detection of features in the hyperspectralscene integrating a deep and convolutional neural network designed todetect at least one sought feature in said hyperspectral scene for aweed or foliar symptom of deficiency or disease from at least onecompressed image of the hyperspectral scene.

According to one embodiment, one, in particular each, detection systemcomprises a system for detecting features in the hyperspectral scenecomprising:

-   -   a neural network configured to calculate a hyperspectral        hypercube of the hyperspectral scene from at least one        compressed image and an uncompressed image of the hyperspectral        scene,    -   a characterization module to detect the weed or the leaf symptom        of deficiency or disease from the hyperspectral hypercube.

According to one embodiment, said agricultural treatment devicecomprises at least one spray nozzle, the flow rate or the pressure ofsaid at least one spray nozzle being controlled by the collaborativedecision of all of said at least two systems for detecting weeds or leafsymptoms of deficiencies or diseases. This embodiment allows a weedingchemical treatment of weeds or treatment of deficiencies or diseases inthe field by optimizing the quantity of phytosanitary product spread inthe agricultural field.

According to one embodiment, said agricultural treatment devicecomprises at least one LASER for destroying weeds, said at least oneLASER being controlled by the collaborative decision of all of said atleast two deficiency or disease foliar symptoms or weeds detectionsystems. This embodiment allows destructive treatment by LASER of theweeds in the field, by optimizing the work rate through the selection ofthe only weeds concerned by the treatment.

According to one embodiment, said agricultural treatment devicecomprises at least one high pressure water jet whose objective is thedestruction of weeds, said at least one high pressure water jet beingcontrolled by the collaborative decision of all of said at least twodeficiency or disease foliar symptoms or weeds detection systems. Thisembodiment allows destructive treatment by high pressure water jet ofthe weeds in the field, by optimizing the work rate by selecting theonly weeds concerned by the treatment.

According to one embodiment, said agricultural treatment devicecomprises at least one hoeing mechanical weeding tool, said at least onehoeing mechanical weeding tool being controlled by the collaborativedecision of all of said at least two deficiency or disease foliarsymptoms or weeds detection systems. This embodiment allows a mechanicaldestructive treatment of the weeds in the field, by optimizing the workrate by selecting the only weeds concerned by the treatment.

According to one embodiment, said agricultural treatment devicecomprises at least one electric weed control tool for destroying weeds,said at least one electric weed control tool being controlled by thecollaborative decision of all of said at at least two deficiency ordisease foliar symptoms or weeds detection systems. This embodimentallows a destructive treatment of electric weeding of the weeds in thefield, by optimizing the work rate by selecting the only weeds concernedby the treatment.

According to one embodiment, the agricultural treatment device islocalised.

According to one embodiment, all of said at least one deficiency ordisease foliar symptoms or weeds detection system is adapted tocollaboratively construct a map of the agricultural field travelled bysaid agricultural machine, said cartography being constructed by ageostatistical process with localised detection data representing thereal state as measured by said at least one deficiency or disease foliarsymptoms or weeds detection system. This embodiment allows thegeneration of a map of the detections of weeds and symptoms ofdeficiencies or diseases in the agricultural plot treated forstatistical purposes and monitoring of treatment of agricultural fields.

According to one embodiment, the device further comprises a controlscreen, and said mapping of the traveled agricultural field is displayedon the control screen intended for the worker processing theagricultural field. This embodiment allows the worker performing thetreatment of the agricultural field to follow in real time theapplication of the treatment in the agricultural field.

According to one embodiment, a processor is adapted to producestatistics on spraying, prevalence, species, densities, or stages ofweeds or leaf symptoms of deficiencies or diseases present in theagricultural field using the mapping of the traveled agricultural field.This embodiment allows monitoring of treatments in the field.

According to one aspect, the invention relates to a collaborativeagricultural processing control method intended to be mounted on anagricultural machine, said agricultural machine comprising at least onecontrollable processing device, the method for controlling agriculturaltreatment including:

-   -   a collaborative decision of said at least one deficiency or        disease foliar symptoms or weeds detection system of which the        detection zones partially overlap, each being suitable for        attachment to the agricultural machine and the localisation of        the treatment to be applied to the detection area; and    -   a communication between said deficiency or disease foliar        symptoms or weeds detection systems with said at least one        treatment device.

According to one embodiment, the collaborative control method of thetreatment device mounted on an agricultural machine on which a set ofdeficiency or disease foliar symptoms or weeds detection systems ismounted, comprises, for each of at least two deficiency or diseasefoliar symptoms or weeds detection systems, the steps of:

-   -   Acquisition of a new image datum from the ground of the        travelled agricultural field on which an agricultural machine        moves by means of said deficiency or disease foliar symptoms or        weeds detection system; and    -   Acquisition of additional position information from said        deficiency or disease foliar symptoms or weeds detection system        by means of an inertial unit and the localisation system; and    -   Projection of said image data acquired by each of said        deficiency or disease foliar symptoms or weeds detection systems        on the ground plane; and    -   Detection of the presence of weeds or leaf symptoms of        deficiencies or diseases from said image data acquired and        projected on said ground plane; and    -   Calculation of the positions of weeds or leaf symptoms of        deficiencies or diseases in the detection zone of said        deficiency or disease foliar symptoms or weeds detection system;        said position calculation using the localisation information of        said localisation system of said deficiency or disease foliar        symptoms or weeds detection system and the detection information        in said image data; and    -   Communication of said positions of weeds or leaf symptoms of        deficiencies or diseases in the detection zone of said        deficiency or disease foliar symptoms or weeds detection system        to all the other deficiency or disease foliar symptoms or weeds        detection systems; and    -   Reception of said positions of weeds or foliar symptoms of        deficiencies or diseases in the detection area of said detector        of weeds or foliar symptoms of deficiencies or diseases from        other deficiency or disease foliar symptoms or weeds detection        systems; and    -   Merging of said positions of weeds or leaf symptoms of        deficiencies or diseases from all the deficiency or disease        foliar symptoms or weeds detection systems; and    -   Calculation of the command to be sent to the treatment device        concerned by the detection zone of said deficiency or disease        foliar symptoms or weeds detection system; and    -   Issuance of the command to the treatment device concerned by the        detection zone of said deficiency or disease foliar symptoms or        weeds detection system.

According to one aspect, said projection uses the information comingfrom said inertial unit of said deficiency or disease foliar symptoms orweeds detection system in order to determine the angle at which theimage data is taken relative to to the normal vector on the ground.

According to one aspect, communication of said positions of weeds orleaf symptoms of deficiencies or diseases in the detection zone of saiddeficiency or disease foliar symptoms or weeds detection system toothers, in particular to the all other deficiency or disease foliarsymptoms or weeds detection systems.

According to one aspect, the fusion is weighted according to the qualityand the calculated distance of each detection.

The invention is assembled on an agricultural machine comprising atleast one controllable processing device. The agricultural machine issuch that said at least two deficiency or disease foliar symptoms orweeds detection systems are fixed on the support of said at least onecontrollable treatment device and communicate with each other and withthe plurality of said at least one controllable processing device for,in operation, issuing the activation control command adapted to bereceived by each of said at least one controllable processing device fortriggering the treatment on the target plant.

With regard to the operation of projecting said image data onto theground plane, the roll, pitch and yaw information is used; this roll,pitch and yaw information being continuously calculated and kept up todate by each of said at least two deficiency or disease foliar symptomsor weeds detection systems by means of an attitude estimation algorithmusing the raw information from said inertial unit on board each of saidat least two deficiency or disease foliar symptoms or weeds detectionsystems. For example, the attitude estimation algorithm, used tocalculate roll, pitch and yaw information, can be an extended Kalmanfilter, a Mahony or Madgwick algorithm. The document “A comparison ofmultisensor attitude estimation algorithm”, A. Cirillo, P. Cirillo, G.De Maria, C. Natale, S. Pirozzi, describes and compares a set of datafusion algorithms from inertial units in order to extract the attitude,defined by the roll, pitch, and yaw angles, of the system.

As a variant, said attitude information can be calculated from the rawinformation from the inertial units of all of said at least twodeficiency or disease foliar symptoms or weeds detection systems. Saidraw information from the inertial units being exchanged by means of thecommunication system continuously connecting said at least twodeficiency or disease foliar symptoms or weeds detection systems, theattitude estimation algorithm executed on each of said at at least twodeficiency or disease foliar symptoms or weeds detection systems can useall of the raw information. Thus, the estimates of roll, pitch and yaware consolidated by a set of similar, consistent and covariant measures.For example, an extended Kalman filter can be used in each of said atleast two deficiency or disease foliar symptoms or weeds detectionsystems, by taking data from the inertial units of all of said at leasttwo deficiency or disease foliar symptoms or weeds detection systems.The document “Data Fusion Algorithms for Multiple Inertial MeasurementUnits”, Jared B. Bancroft and Gérard Lachapelle, Sensors (Basel), Jun.29, 2011, 6771-6798, presents an alternative algorithm for merging rawdata from a set of inertial units to determine attitude information.

As a variant, said attitude information can be calculated from the rawinformation of the inertial units to which the geolocalisation data ofall of said at least two deficiency or disease foliar symptoms or weedsdetection systems are added. Said raw information from the inertialunits as well as the geolocalisation data being exchanged by means ofthe communication system connecting the said at least two deficiency ordisease foliar symptoms or weeds detection systems, the attitudeestimation algorithm can use all of the raw information. For example, anextended Kalman filter can be used in each of said at least twodeficiency or disease foliar symptoms or weeds detection systems, takingthe data from inertial units as well as the geolocalisation data of theset of said at least two deficiency or disease foliar symptoms or weedsdetection systems. Furthermore, a method, as described in the document“Attitude estimation for accelerated vehicles using GPS/INSmeasurements”, Minh-Duc Hua, July 2010, Control Engineering PracticeVolume 18, Issue 7, July 2010, pages 723-732, allows a fusion ofinformation from a geolocalisation system and an inertial unit.

Said projection on the ground of said image data is calculated accordingto the following relationships:

Img_(projected) = R⁻¹ ⋅ Img_(acquired) R = Rz ⋅ Ry ⋅ Rx$R_{x} = \begin{bmatrix}1 & 0 & 0 \\0 & {\cos\;\gamma} & {{- \sin}\;\gamma} \\0 & {\sin\;\gamma} & {\cos\;\gamma}\end{bmatrix}$ $R_{y} = \begin{bmatrix}{\cos\;\beta} & 0 & {\sin\;\beta} \\0 & 1 & 0 \\{{- \sin}\;\beta} & 0 & {\cos\;\beta}\end{bmatrix}$ $R_{z} = \begin{bmatrix}{\cos \propto} & {{- \sin} \propto} & 0 \\{\sin \propto} & {\cos \propto} & 0 \\0 & 0 & 1\end{bmatrix}$

Where:

-   -   lmg_(projected) is the tensor containing the pixels of the image        projected on the ground; and    -   lmg_(acquired) is the tensor containing the pixels of said raw        image data; and    -   R is the matrix containing the rotations along the three roll        axes, pitch and yaw; and    -   α is the yaw angle; and    -   β is the roll angle; and    -   γ is the pitch angle.

Said image data projected on the ground is used to detect the presenceof weeds or leaf symptoms of deficiencies or diseases from the specificcharacteristics of weeds or leaf symptoms of deficiencies or diseases inorder to detect the areas in said image data in which the target plantsare present. Each of the detections of the presence of weeds or leafsymptoms of deficiencies or diseases is supplemented with a probabilityof the presence of said peculiarities specific to weeds or leaf symptomsof deficiencies or diseases. This probability information is necessaryfor geostatistical calculations to decide on the application of atreatment on the target plant. For example, a hyperspectral sensor, asdescribed in the document FR1873313, “Detection device withhyperspectral acquisition” or in the document FR1901202, “Hyperspectraldetection device by fusion of sensors”, or in the document FR1905916,“Detection device hyperspectral” can be used to detect theparticularities sought for weeds or leaf symptoms of deficiencies ordiseases.

With regard to the calculation of the positions of weeds or leafsymptoms of deficiencies or diseases, the detection of peculiaritiesspecific to weeds or leaf symptoms of deficiencies or diseases in saidprojected image data indicates the presence of said target plants in thecoordinate system of said projected image data. In addition, each of theprojected image data is geolocalised from geolocalisation informationobtained by means of said geolocalisation system of said deficiency ordisease foliar symptoms or weeds detection system. Said obtainedgeolocalisation information corresponds to the position of saiddeficiency or disease foliar symptoms or weeds detection system at thetime of capturing said image data. Said ground projection operation isapplied to said geolocalisation information in order to obtain theprojected coordinates on the ground of said projected image data. Thusthe contours of the detection of said peculiarities specific to weeds orfoliar symptoms of deficiencies or diseases detected on each of said atleast two deficiency or disease foliar symptoms or weeds detectionsystems are geolocalised in the agricultural plot.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems obtains continuously, and by means of thecommunication system between the various deficiency or disease foliarsymptoms or weeds detection systems, the geolocalised detectioninformation of all the other deficiency or disease foliar symptoms orweeds detection systems. All of the information for said detections ofweeds or leaf symptoms of deficiencies or diseases from all of said atleast two deficiency or disease foliar symptoms or weeds detectionsystems is stored in a geographic database local to each of said atleast two deficiency or disease foliar symptoms or weeds detectionsystems.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems calculates the geostatistics in real time of thepresence of weeds or leaf symptoms of deficiencies or diseases from theset said geolocalised information on the detection of weeds or leafsymptoms of deficiencies or diseases and for which probability ofpresence information is provided. The computation of geostatistics usesa krigeage algorithm, as described in the book “Lognormal-de WijsianGeostatistics for Ore Evaluation”, D. G. Krige, 1981, ISBN978-0620030069; Said krigeage algorithm making it possible toconsolidate said information for detecting weeds or foliar symptoms ofdeficiencies or diseases from all of said at least two deficiency ordisease foliar symptoms or weeds detection systems taking into accountthe respective probabilities of each of said detection. When saiddetection information for weeds or foliar symptoms of deficiencies ordiseases consolidated by means of said geostatistical calculationconfirms the presence of the desired characteristic of the weed orfoliar symptoms of deficiency or disease, the geolocalisation detectioninformation is added to the list of target plants to be treated.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems continuously calculates the instantaneous speed ofmovement by means of said geolocalisation information obtained by meansof said geolocalisation system. The speed information is necessary inorder to estimate the order time of said at least one agriculturalprocessing device and to anticipate the processing time as a function ofsaid agricultural processing device.

With regard to the calculation of the order to be sent to said at leastone agricultural treatment device, each of said at least two deficiencyor disease foliar symptoms or weeds detection systems estimates at alltimes, and for each of said target plants currently in range of said atleast one treatment device, which of said at least one treatment deviceis most suitable for treating said target plant; For example, thespreading nozzle closest to the target plant is selected when said atleast one treatment device is a spreading boom. Likewise, the treatmenttool closest to the target plant can be selected. This determinationuses the location data of the treatment device, expressed in the frameof reference of the field in which the weeds or leaf symptoms ofdeficiencies or diseases are geolocalised.

The control commands are transmitted to said at least one agriculturaltreatment device by means of communication between said at least twodeficiency or disease foliar symptoms or weeds detection systems andsaid at least one agricultural treatment device.

With regard to controlling said at least one agricultural treatmentdevice, all of the information from said detections of weeds or leafsymptoms of deficiencies or diseases is geolocalised and said at leastone agricultural treatment device are actuated at the exact instant whensaid at least one agricultural treatment device is above the targetplants.

In one aspect, the computerized methods described here are implementedby one or more computer programs executed by a processor of aprogrammable machine.

SUMMARY DESCRIPTION OF THE FIGURES

The manner of carrying out the invention as well as the advantages whichensue therefrom will emerge clearly from the embodiment which follows,given by way of indication but not limitation, in support of theappended figures in which FIGS. 1 to 17 represent:

FIG. 1: a schematic representation of the complete device; and

FIG. 2: a schematic structural representation of the elements of thedevice of FIG. 1;

FIG. 3: a schematic front view of a device for capturing a hyperspectralimage according to an embodiment of the invention;

FIG. 4: a schematic structural representation of the elements of thedevice of FIG. 3;

FIG. 5: a schematic representation of the influence weights of theneural network of FIG. 4;

FIG. 6: a schematic representation of the architecture of the neuralnetwork of FIG. 4.

FIG. 7: a schematic front view of the elements of a capture anddetection device in a hyperspectral scene according to an embodiment ofthe invention;

FIG. 8: a schematic structural representation of the elements of thedevice of FIG. 7;

FIG. 9: an alternative structural schematic representation of theelements of the device of FIG. 7;

FIG. 10: a schematic representation of the diffractions obtained by theacquisition device of FIG. 8;

FIG. 11: a schematic representation of the architecture of the neuralnetwork of FIG. 8.

FIG. 12: a schematic front view of the elements of a capture anddetection device in a hyperspectral scene according to a secondembodiment of the invention;

FIG. 13: a schematic structural representation of the elements of thedevice of FIG. 12;

FIG. 14: a schematic representation of the architecture of the neuralnetwork of FIG. 13.

FIG. 15: a schematic structural representation, seen in projection, ofthe elements of the device of FIG. 1;

FIG. 16: a graph showing a collaborative piloting process foragricultural treatment devices; and

FIG. 17 is a schematic representation similar to FIG. 15 for anotherembodiment.

DETAILED DESCRIPTION

By “direct”, when we qualify the detection of feature, we thus describethat the result of output from the detection system is the soughtfeature. We exclude here the cases where the output of the detectionsystem does not correspond to the sought feature, but only correspondsto an intermediary in the calculation of the feature. However, theoutput from the direct detection system can, in addition tocorresponding to the feature sought, also be used for subsequentprocessing. In particular, by “direct”, it is meant that the output ofthe feature detection system is not a hyperspectral cube of the scenewhich, in itself, does not constitute a feature of the scene.

By “compressed”, we mean a two-dimensional image of a three-dimensionalscene comprising spatial and spectral information of thethree-dimensional scene. The spatial and spectral information of thethree-dimensional scene is thus projected by means of an optical systemonto a two-dimensional capture surface. Such a “compressed” image mayinclude one or more diffracted images of the three-dimensional scene, orparts thereof. In addition, it can also include part of a non-diffractedimage of the scene. Thus, the term “compressed” is used because atwo-dimensional representation of three-dimensional spectral informationis possible. By “spectral”, we understand that we go beyond, in terms ofthe number of frequencies detected, a “standard” RGB image of the scene.

By “standard”, we refer, as opposed to a “compressed” image, to an imageexhibiting no diffraction of the hyperspectral scene. However, such animage can be obtained by optical manipulations using reflecting mirrorsor lenses.

By “non-homogeneous”, we refer to an image whose properties are notidentical on the whole image. For example, a “non-homogeneous” image cancontain, at certain locations, pixels whose information essentiallycomprises spectral information at a certain respective wavelength band,as well as, in other locations, pixels the information of whichessentially includes non-spectral information. Computer processing ofsuch a “non-homogeneous” image is not possible, because the propertiesnecessary for its processing are not identical depending on thelocations in this image.

By “characteristic”, we mean a characteristic of the scene—thischaracteristic can be spatial, spectral, correspond to a shape, a color,a texture, a spectral signature or a combination of these, and can inparticular be interpreted semantically.

“Object” refers to the common meaning used for this term. Objectdetection on an image corresponds to the location and a semanticinterpretation of the presence of the object on the imaged scene. Anobject can be characterized by its shape, color, texture, spectralsignature or a combination of these characteristics.

FIG. 1 illustrates a cooperative agricultural treatment control deviceintended to be mounted on an agricultural machine 1, said agriculturalmachine 1 comprising at least one controllable agricultural treatmentdevice 3; said cooperative agricultural treatment control devicecomprising at least two deficiency or disease foliar symptoms or weedsdetection systems 2, each being mechanically adapted for attachment tothe agricultural machine 1 and having a target viewing angle acquisitionin the direction of advancement of said agricultural machine 1. As canbe seen in particular in FIG. 1, the agricultural machine moves in theagricultural field 5 in a direction of advance. The detection systems 2can be arranged spaced from each other in a horizontal directiontransverse to the direction of advance. They can for example be carriedby a transverse beam of the agricultural machine. To fix the ideas, wecan define the “x” axis as the advancement axis of the agriculturalmachine, and “y” the horizontal transverse axis (substantially parallelto the main direction of the beam). By “horizontal” is meant the meanplane of the ground at the level of the agricultural machine. Theagricultural treatment device 3 is controllable for treating an area tobe treated downstream of the area imaged by the deficiency or diseasefoliar symptoms or weeds detection system 2 along the movement of theagricultural machine.

As illustrated in FIG. 2, the plurality of said at least two deficiencyor disease foliar symptoms or weeds detection systems 2 is fixed on theagricultural machine so as to capture the visual information of theagricultural plot 5. Each of said at least two deficiency or diseasefoliar symptoms or weeds detection systems 2 has a detection fieldoverlapping the detection field of at least one neighboring deficiencyor disease foliar symptoms or weeds detection system 2. For deficiencyor disease foliar symptoms or weeds detection systems which are notplaced at the ends of the beam, their detection field may overlap thedetection field of at least two deficiency or disease foliar symptoms orweeds detection systems 2. For example, a hyperspectral sensor, asdescribed in the document FR1873313, “Detection device withhyperspectral acquisition” or in the document FR1901202, “Hyperspectraldetection device by fusion of sensors”, or in the document FR1905916,“Detection device hyperspectral “, or in document WO2019EP85847,”Hyperspectral detection device “, can be used for each of said at leasttwo deficiency or disease foliar symptoms or weeds detection systems 2.

According to a first embodiment, the deficiency or disease foliarsymptoms or weeds detection system 2 comprises a capture device 10 and acomputerized characterization module 21. FIG. 3 illustrates a capturedevice 10 of a hyperspectral image 15 in three dimensions comprisingthree juxtaposed sensors 11-13. A first sensor 11 makes it possible toobtain a compressed image 14′ of a focal plane P11′ of an observedscene. As illustrated in FIG. 4, this first sensor 11 comprises a firstconverging lens 30 which focuses the focal plane P11′ on an opening 31.A collimator 32 captures the rays passing through the opening 31 andtransmits these rays to a diffraction grating 33. A second converginglens 34 focuses these rays from the diffraction grating 33 on acollection surface 35.

The structure of this optical network is relatively similar to thatdescribed in the scientific publication “Computed-tomography imagingspectrometer: experimental calibration and reconstruction results”,published in APPLIED OPTICS, volume 34 (1995) number 22.

This optical structure makes it possible to obtain a compressed image14′, illustrated in FIG. 5, having several diffractions R0-R7 of thefocal plane P11′ arranged around a small non-diffracted image. In theexample of FIGS. 3 to 6, the compressed image has eight distinct R0-R7diffractions obtained with two diffraction axes of the diffractiongrating 33 arranged as far apart as possible from one another in a planenormal to the optical axis, that is to say substantially orthogonal toone another.

Alternatively, three axes of diffraction can be used on the diffractiongrating 33 so as to obtain a diffracted image 14′ with sixteendiffractions. The three diffraction axes can be equally distributed,that is to say separated from each other by an angle of 60°.

Thus, in general, the compressed image comprises 2R+1 diffractions ifone uses R evenly distributed diffraction gratings, that is to sayseparated by the same angle from each other.

The capture surface 35 can correspond to a CCD sensor (for“charge-coupled device” in English, that is to say a charge transferdevice), to a sensor CMOS (for “complementary metal-oxide-semiconductor”in Anglo-Saxon literature, a technology for manufacturing electroniccomponents), or any other known sensor. For example, the scientificpublication “Practical Spectral Photography”, published in Eurographics,volume 31 (2012) number 2, proposes to combine this optical structurewith a standard digital camera to capture the compressed image.

Preferably, each pixel of the compressed image 14′ is coded on 8 bitsthus making it possible to represent 256 colors.

A second sensor 12 makes it possible to obtain a non-diffracted image17′ of a focal plane P12′ of the same observed scene, but with an offsetinduced by the offset between the first 11 and the second sensor 12.This second sensor 12 corresponds to an RGB sensor, that is to say asensor making it possible to code the influence of the three colors Red,Green and Blue of the focal plane P12′. It makes it possible to accountfor the influence of the use of a blue filter F1, a green filter F2 anda red filter F3 on the observed scene.

This sensor 12 can be produced by a CMOS or CCD sensor associated with aBayer filter. Alternatively, any other sensor can be used to acquirethis RGB image 17′. Preferably, each color of each pixel of the RGBimage 17′ is coded on 8 bits. Thus, each pixel of the RGB image 17′ iscoded on 3 times 8 bits. Alternatively, a monochrome sensor could beused.

A third sensor 13 makes it possible to obtain an infrared image 18′, IR,of a third focal plane P13′ of the same observed scene with also anoffset with the first 11 and the second sensors 12. This sensor 13 makesit possible to account for the influence of the use of an infraredfilter F4 on the observed scene.

Any type of known sensor can be used to acquire this IR image 18.Preferably, each pixel of the IR image 18 is coded on 8 bits.Alternatively, only one or the other of sensor 12 and sensor 13 is used.

The distance between the three sensors 11-13 can be less than 1 cm so asto obtain a significant overlap of the focal planes P11′-P13′ by thethree sensors 11-13. The sensors are for example aligned along the xaxis. The topology and the number of sensors can vary without changingthe invention.

For example, the sensors 11-13 can acquire an image of the same observedscene by using semi-transparent mirrors to transmit the information ofthe scene observed to the various sensors 11-13. FIG. 3 illustrates adevice 10 comprising three sensors 11-13. As a variant, other sensorscan be mounted on the device 10 to increase the information contained inthe hyperspectral image. For example, the device 10 can integrate asensor whose wavelength is between 0.001 nanometer and 10 nanometers ora sensor whose wavelength is between 10,000 nanometer and 20,000nanometers.

As illustrated in FIG. 4, the device 10 also includes a constructionmodule 16 for a hyperspectral image 15 from the different diffractionsR0-R7 of the diffracted image 14′ and the non-diffracted 17′, 18′.

In the example of FIGS. 3 to 6, in which the sensors 11-13 arejuxtaposed, a preprocessing step is carried out to extract a focal planeP11 P13 present on each of the images 14′, 17′-18′acquired by the threesensors 11-13. This pretreatment consists, for each focal planeP11′-P13′, in isolating the common part from the focal planes P11′-P13′then extracting 26 this common part to form the image 14, 17-18 of eachfocal plane P11-P13 observed by the specific sensor 11-13. The part ofeach image 14′, 17′-18′ to be isolated can be defined directly in amemory of the capture device 10 according to the respective positioningchoices for the sensors 11-13, or a learning step can be used toidentify the part to be isolated 25.

Preferably, the images 17′-18′ from RGB and IR sensors are cross-checkedusing a cross-correlation in two dimensions. The extraction of the focalplane of the diffracted image 14′ is calculated by interpolation of thex and y offsets between the sensors 12-13 with reference to the positionof the sensor 11 of the diffracted image by knowing the distance betweeneach sensor 11-13. This preprocessing step is not always necessary, inparticular, when the sensors 11-13 are configured to capture the samefocal plane, for example with the use of semi-transparent mirrors.

When the images 14, 17 and 18 of each focal plane P11-P13 observed byeach sensor 11-13 are obtained, the construction module 16 implements aneural network 20 to form a hyperspectral image 15 from the informationin these three images 14, 17-18.

This neural network 20 aims at determining the intensity I_(X,Y,λ) ofeach voxel V_(X,Y,λ) of the hyperspectral image 15.

To do this, as illustrated in FIG. 6, the neural network 20 comprises aninput layer 40, capable of extracting the information from the images14, 17-18, and an output layer 41, able to process this information soas to create information for the considered voxel V_(X,Y,λ).

The first neuron of the input layer 40 makes it possible to extract theintensity I_(IR)(x,y) from the IR image 18 as a function of the x and ycoordinates of the sought voxel V_(X,Y,λ). For example, if the IR image18 is coded on 8 bits, this first neuron transmits to the output layer41 the 8-bit value of the pixel of the IR image 18 at the sought x and ycoordinates. The second neuron of the input layer 40 performs the sametask for the red color 17 a of the RGB image 17.

According to the previous example, each color being coded on 8 bits, thesought intensity I_(R)(x; y) is also coded on 8 bits. The third neuronsearches for the intensity I_(V)(x; y) in the same way for the greencolor 17 b and the fourth neuron searches for the intensity I_(B)(x; y)for the blue color 17 c. Thus, for these first four neurons, it is veryeasy to obtain the intensity, because it is enough to use the positionin x and y of the desired voxel.

The following neurons of the input layer 40 are more complex, since eachof the following neurons is associated with a diffraction R0-R7 of thediffracted image 14.

These neurons seek the intensity of a specific diffraction I_(n)(x, y)as a function of the position in x and y, but also of the wavelength λof the sought voxel V_(X,Y,λ).

This relation between the three coordinates of the voxel V_(X,Y,λ) andthe position in x and y can be coded in a memory during the integrationof the neural network 20.

Preferably, a learning phase makes it possible to define thisrelationship using a known model, the parameters of which are soughtfrom representations of known objects. An example model is defined bythe following relation:

$\quad\begin{Bmatrix}{x_{n} = {x + {x_{offsetX}(n)} + {\lambda \cdot \lambda_{sliceX}}}} \\{y_{n} = {y + {y_{offsetY}(n)} + {\lambda \cdot \lambda_{sliceY}}}}\end{Bmatrix}$

with:n=floor (M(dt−1)/DMAX);n between 0 and M, the number of diffractions of the compressed image;λ=(dt−1)mod(DMAX/M);dt between 1 and DMAX;xt between 0 and XMAX;yt between including between 0 and YMAX;XMAX the size along the x axis of the tensor of order three of the inputlayer;YMAX the size along the y axis of the tensor of order three of the inputlayer;DMAX the depth of the tensor of order three of the input layer;λ_(sliceX), the spectral step constant along the x axis of saidcompressed image;λ_(sliceY), the spectral step constant along the y axis of saidcompressed image;x_(offsetX)(n) corresponding to the offset along the x axis of thediffraction n;y_(offsetY)(n) corresponding to the offset along the y axis of thediffraction n.Floor is a well-known truncation operator.Mod represents the mathematical operator modulo.

A learning phase therefore makes it possible to define the parametersλ_(sliceX), λ_(sliceY), x_(offsetx)(n), and v_(offsetY)(n), so that eachneuron can quickly find the intensity of the corresponding pixel. As avariant, other models are possible, in particular depending on thenature of the used diffraction grating 33.

In addition, the information related to the intensity of the pixelI_(n)(x, y) sought by each neuron can be determined by a product ofconvolution between the intensity of the pixel of the compressed image14 and of its close neighbors in the different R0-R7 diffractions.According to the previous example, the output of these neurons from theinput layer 40 is also coded on 8 bits.

All these different intensities of the input layer 40 are injected intoa single neuron of the output layer 41 which has the function ofcombining all this information and of providing the value of theintensity l_(X,Y,λ) of the desired voxel.

To do this, this output neuron 41 associates a weight with each item ofinformation as a function of the wavelength λ of the voxel sought.Following this modulation on the influence of the contributions of eachimage 17-18 and of each diffraction R0-R7, this output neuron 41 can addup the contributions to determine an average intensity which will formthe intensity I_(x,y,λ) of the sought voxel V_(X,Y,λ), for example codedon 8 bits. This process is repeated for all the coordinates of the voxelV_(X,Y,λ), so as to obtain a hypercube containing all the spatial andspectral information originating from the non-diffracted images 17-18and from each diffraction R0-R7. For example, as illustrated in FIG. 5,to find the intensity I_(x,y,λ) of a voxel V_(X,Y,λ) whose wavelength is500 nm, that is to say a wavelength between that of blue (480 nm) andthat of green (525 nm), the output neuron 41 will use the spatialinformation of the non-diffracted images obtained with blue F1 and greenF2 filters as well as the information of the obtained different R0-R7diffractions as a function of the wavelength considered. It is possibleto configure the neural network 20 so as not to take some of thediffractions R0-R7 into account so as to limit the time for calculatingthe sum of the contributions. In the example of FIG. 5, the thirddiffraction R2 is not considered by the neuron of the output layer 41.The weight of each contribution as a function of the wavelength λ of thesought voxel V_(X,Y,λ) can also be defined during the implantation ofthe neural network 20 or determined by a learning phase. Learning can becarried out by using known scenes captured by the three sensors 11-13and by determining the weights of each contribution for each wavelengthλ so that the information from each known scene corresponds to theinformation contained in the known scenes. This learning can be carriedout independently or simultaneously with learning the relationshipsbetween the three coordinates of the voxel V_(X,Y,λ) and the position inx and y on the diffracted image 14. This neural network 20 can beimplemented in an on-board system so as to process in real time theimages from the sensors 11-13 to define and store a hyperspectral image15 between two acquisitions of the sensors 11-13. For example, theon-board system may include a power supply for the sensors 11-13, aprocessor configured to perform the calculations of the neurons of theinput layer 40 and the output layer 41 and a memory integrating theweights of each neuron of the input layer 40 as a function of thewavelength λ. As a variant, the different treatments can be carried outindependently on several electronic circuits without changing theinvention. For example, an acquisition circuit can acquire and transmitthe information originating from the neurons of the first layer 40 to asecond circuit which contains the neuron of the second layer 41.

The invention thus makes it possible to obtain a hyperspectral image 15quickly and with great discretization in the spectral dimension. The useof a neural network 20 makes it possible to limit the complexity of theoperations to be carried out during the analysis of the diffracted image14. In addition, the neural network 20 also allows the association ofthe information of this diffracted image 14 with those of non-diffractedimages 17-18 to improve the precision in the spatial dimension.

A computerized characterization module 21 is used downstream todetermine a weed or a leaf symptom of deficiency or disease. Forexample, the input of the computerized characterization module is thehyperspectral image 15 in three dimensions. The computerizedcharacterization module can for example apply a predefined treatment,characterizing the weed or the leaf symptom of deficiency or disease, tothe hyperspectral image 15 in three dimensions, and outputting apresence or absence of the weed or the leaf symptom of deficiency ordisease.

The computerized characterization module can for example apply, asdescribed in the article “Hyperspectral image analysis techniques forthe detection and classification of the early onset of plant disease andstress”, Amy Lowe, Nicola Harrison and Andrew P. French, Plant Methods(2017), an index-based detection (for example the “Normalized DifferenceVegetation Index”—NDVI—or “Photochemical Reflectance Index” (PRI)), inorder to pre-process the hyperspectral image 15 in three dimensions byselecting a subset of spectral bands which are assembled by means of anindex. For example, the PRI index is a two-dimensional image composed ofthe bands at 531 nm and 570 nm by the equationlmg=(R₅₃₁−R₅₇₀)/(R₅₃₁+R₅₇₀), where R_(n) represents the intensity of thevoxel with coordinates (x; y; n) of the hyperspectral cube. Theresulting image identifies the presence of plants in the image. Thevalue in one pixel is compared to a pre-defined scale to classify thedetection in this pixel. Typically, in the resulting image, a value in apixel of between −0.2 and 0.2 indicates the presence of a healthy plantin this pixel.

Other indices are applicable, each one making it possible to process thehyperspectral image and to detect the presence either of a weed, or of aleaf symptom of deficiency or disease, or the presence of a plant. Thepotentially applicable indices include the following:

-   -   “Normalized difference vegetation index” (NDVI), defined by the        equation: (RNIR−RRED)/(RNIR+RRED), with RRED=680 nm, RNIR=800        nm, used to detect the presence of plants;    -   “Red edge” NDVI, defined by the equation        (R₇₅₀−R₇₀₅)/(R₇₅₀+R₇₀₅), used to detect the presence of plants;    -   “Simple ratio index” (SRI), defined by the equation RNIR/RRED        with RRED=680 nm, RNIR=800 nm, allowing to detect the presence        of plants;    -   “Photochemical reflectance index” (PRI), defined by the equation        (R₅₃₁−R₅₇₀)/(R₅₃₁+R₅₇₀), used to detect the vigor (or good        health) of a plant;    -   “Plant senescence reflectance index” (PSRI), defined by the        equation (Red Green)/NIR, where Red represents the sum of the        intensities of the voxels with wavelengths between 620 and 700        nm, Green represents the sum of intensities of voxels with        wavelengths between 500 and 578 nm, NIR represents the sum of        the intensities of voxels with wavelengths between 700 and 1000        nm, making it possible to detect the senescence of a plant, the        stress of a vegetable or the maturity of a fruit;    -   “Normalized phaeophytinization index” (NPQI), defined by the        equation (R₄₁₅−R₄₃₅)/(R₄₁₅+R₄₃₅), used to measure the        degradation of leaf chlorophyll;    -   “Structure Independent Pigment Index” (SIPI), defined by the        equation (R₈₀₀−R₄₄₅)/(R₈₀₀+R₆₈₀), used to detect the vigor (or        good health) of a plant; and    -   “Leaf rust disease severity index” (LRDSI), defined by equation        6.9×(R₆₀₅/R₄₅₅)−1.2, used to detect rust disease in wheat        leaves.

Any other index suitable for detecting a particular disease or stresscan be used.

If applicable, the predefined equation gives a probability of thepresence of the weed or the foliar symptom of deficiency or disease. Ifnecessary, an additional output from the computerized characterizationmodule is a localisation of the weed or the leaf symptom of deficiencyor disease in image 17 or 18.

In the context of the present patent application, the detection systemdescribed above is considered to be a single detection system, even ifit uses different sensors whose information is merged to detect a weedor deficiency or disease leaf syndrome.

According to a second embodiment, the deficiency or disease foliarsymptoms or weeds detection system 2 comprises a capture device 202.FIG. 7 illustrates a device 202 for capturing a hyperspectral scene 203comprising a sensor, or acquisition system 204, making it possible toobtain a compressed image in two dimensions 211 of a focal plane 303 ofan observed scene. The hyperspectral scene can be identified in space bymeans of an orthonormal coordinate system (x; y; z) not shown.

As illustrated in FIG. 8, the capture device 202 is similar to thatdescribed above.

This optical structure makes it possible to obtain a compressed image211, illustrated in FIG. 10, presenting several diffractions R0-R7 ofthe focal plane 303 arranged around a small non-diffracted image C.

Alternatively, as illustrated in FIG. 9, the capture device 202 cancomprise a first converging lens 241 which focuses the focal plane 303on a mask 242. A collimator 243 captures the rays passing through themask 242 and transmits these rays to a prism 244. A second converginglens 245 focuses these rays coming from the prism 244 on a collectionsurface 246. The mask 242 defines a coding for the image 213.

The structure of this optical assembly is relatively similar to thatdescribed in the scientific publication “Compressive Coded ApertureSpectral Imaging”, IEEE Signal Processing Magazine, Volume 31, Issue 1,Gonzalo R. Arce, David J. Brady, Lawrence Carin, Henry Arguello, andDavid S. Kittle.

Alternatively, the capture surfaces 35 or 246 can correspond to thephotographic acquisition device of a smartphone or any other portabledevice including a photographic acquisition arrangement, by adding thecapture device 202 of the hyperspectral scene 203 in front of thephotographic acquisition device.

As a variant, the acquisition system 204 may include a compactmechanical embodiment which can be integrated into a portable andautonomous device and the detection system is included in said portableand autonomous device.

Alternatively, the capture surfaces 35 or 246 can be a device whosewavelengths captured are not in the visible part. For example, thedevice 202 can integrate sensors whose wavelength is between 0.001nanometer and 10 nanometers or a sensor whose wavelength is between10,000 nanometers and 20,000 nanometers, or a sensor whose length waveis between 300 nanometers and 2000 nanometers. It can be an infrareddevice.

When the image 211 of the observed hyperspectral focal plane isobtained, the detection system 2 implements a neural network 212 todetect a particular feature in the observed scene from the informationof the compressed image 211.

This neural network 212 aims at determining the probability of thepresence of the characteristic sought for each pixel localised at the xand y coordinates of the observed hyperspectral scene 203.

To do this, as illustrated in FIG. 11, the neural network 212 comprisesan input layer 230, capable of extracting the information from the image211 and an output layer 231, capable of processing this information soas to generate an image whose intensity of each pixel at the x and ycoordinates, corresponds to the probability of the presence of thefeature at the x and y coordinates of the hyperspectral scene 203.

The input layer 230 is populated from the pixels forming the compressedimage. Thus, the input layer is a tensor of order three, and has twospatial dimensions of size X_(MAX) and Y_(MAX), and a depth dimension ofsize D_(MAX), corresponding to the number of subsets of the compressedimage copied into the input layer. The invention uses the nonlinearrelation f(x_(t), y_(t), d_(t))→(x_(img), y_(ing)) defined for x_(t)ϵ[0. . . X_(MAX)[, y_(t)ϵ[0 . . . Y_(MAX)[and d_(t)ϵ[0 . . .D_(MAX)[allowing to calculate the x_(img) and y_(img) coordinates of thepixel of the compressed image whose intensity is copied into the tensorof order three of said input layer of the neural network at thecoordinates (x_(t), y_(t), d_(t)).

For example, in the case of a compressed image 211 obtained from thecapture device of FIG. 8, the input layer 230 can be populated asfollows:

${f\left( {x_{t},y_{t},d_{t}} \right)} = \begin{Bmatrix}{x_{img} = {x + {x_{offsetX}(n)} + {\lambda \cdot \lambda_{sliceX}}}} \\{y_{img} = {y + {y_{offsetY}(n)} + {\lambda \cdot \lambda_{sliceY}}}}\end{Bmatrix}$

with:n=floor (M(d_(t)−1)/D_(MAX));n between 0 and M, the number of diffractions of the compressed image;λ=(d_(t)−1)mod(D_(MAX)/M);d_(t) between 1 and D_(MAX);x_(t) between 0 and X_(MAX);y_(t) between 0 and Y_(MAX);X_(MAX) the size along the x axis of the tensor of order three of theinput layer;Y_(MAX) the size along the y axis of the tensor of order three of theinput layer;D_(MAX) the depth of the tensor of order three of the input layer;λ_(sliceX), the spectral step constant along the x axis of saidcompressed image;λ_(sliceY), the spectral step constant along the y axis of saidcompressed image;x_(offsetX(n)) corresponding to the offset along the x axis of thediffraction n;y_(offsetY(n)) corresponding to the offset along the y axis of thediffraction n.Floor is a well-known truncation operator.

Mod represents the mathematical operator modulo.

As is clearly visible in FIG. 11, each slice, in depth, of the inputtensor of order three of the neural network, receives part of adiffraction lobe corresponding substantially to a range of wavelengths.

Alternatively, the invention makes it possible to correlate theinformation contained in the different diffractions of the diffractedimage with information contained in the non-diffracted central part ofthe image.

According to this variant, an additional slice can be added in thedirection of the depth of the input layer, the neurons of which will bepopulated with the intensity detected in the pixels of the compressedimage corresponding to the non-diffracted detection. For example, if weassign to this slice the coordinate d_(t)=0, we can keep the aboveformula for populating the input layer for d_(t) greater than or equalto 1, and populate the layer d_(t)=0 in the following way:

x _(img)=(lmg _(width)/2)−X _(MAX) +x _(t);

y _(img)=(lmg _(height)/2)−Y _(MAX) +y _(t);

With:

lmg_(width) the size of the compressed image along the x axis;lMg_(height) the size of the compressed image along the y axis.

The compressed image obtained by the optical system contains the focalplane of the non-diffracted scene in the center, as well as thediffracted projections along the axes of the different diffractionfilters. Thus, the neural network uses, for the direct detection of thesought features, the following information of said at least onediffracted image:

-   -   the light intensity in the central and non-diffracted part of        the focal plane of the scene at the x and y coordinates; and    -   light intensities in each of the diffractions of said compressed        image whose coordinates x′ and y′ are dependent on the        coordinates x and y of the non-diffracted central part of the        focal plane of the scene.

As a variant, in the case of a compressed image 213 obtained from thecapture device of FIG. 9, the input layer 230 can be populated asfollows:

f(x _(t) ,y _(t) ,d _(t))={(x _(img) =x _(t));(y _(img) =y_(t))}(lmg=MASK if d _(t)=0;lmg=CASSI if d _(t)>0),

With:

MASK: image of the compression mask used,CASSI: measured compressed image,lmg: Selected image from which the pixel is copied.

On slice 0 of the tensor of order three of the input layer the image ofthe used compression mask is copied.

The compressed slices of the hyperspectral scene are copied from theother slices of the tensor of order three of the input layer.

The architecture of said neural network 212, 214 is composed of a set ofconvolutional layers assembled linearly and alternately with decimation(pooling) or interpolation (unpooling) layers.

A convolutional layer of depth d, denoted CONV (d), is defined by dconvolution kernels, each of these convolution kernels being applied tothe volume of the input tensor of order three and of sizex_(input),y_(input),d_(input). The convolutional layer thus generates anoutput volume, tensor of order three, having a depth d. An ACTactivation function is applied to the calculated values of the outputvolume of this convolutional layer.

The parameters of each convolution kernel of a convolutional layer arespecified by the learning procedure of the neural network.

Different ACT activation functions can be used. For example, thisfunction can be a ReLu function, defined by the following equation:

ReLu(x)=max(0,x)

Alternating with the convolutional layers, decimation layers (pooling),or interpolation layers (unpooling) are inserted.

A decimation layer makes it possible to reduce the width and height ofthe tensor of order three at the input for each depth of said tensor oforder three. For example, a MaxPool decimation layer (2,2) selects themaximum value of a sliding tile on the surface of 2×2 values. Thisoperation is applied to all the depths of the input tensor and generatesan output tensor having the same depth and a width divided by two, aswell as a height divided by two.

An interpolation layer makes it possible to increase the width and theheight of the tensor of order three as input for each depth of saidtensor of order three. For example, a MaxUnPool(2,2) interpolation layercopies the input value of a sliding point onto the surface of 2×2 outputvalues. This operation is applied to all the depths of the input tensorand generates an output tensor having the same depth and a widthmultiplied by two, as well as a height multiplied by two.

A neural network architecture allowing the direct detection of featuresin the hyperspectral scene can be as follows:

Input

-   -   CONV(64)    -   MaxPool(2,2)    -   CONV(64)    -   MaxPool(2,2)    -   CONV(64)    -   MaxPool(2,2)    -   CONV(64)    -   CONV(64)    -   MaxUnpool(2,2)    -   CONV(64)    -   MaxUnpool(2,2)    -   CONV(64)    -   MaxUnpool(2,2)    -   CONV(1)    -   Output

As a variant, the number of convolution CONV(d) and MaxPool(2,2)decimation layers can be modified in order to facilitate the detectionof features having a higher semantic complexity. For example, a highernumber of convolution layers makes it possible to process more complexsignatures of shape, texture, or spectral of the feature sought in thehyperspectral scene.

Alternatively, the number of deconvolution CONV (d) and MaxUnpool(2, 2)interpolation layers can be changed to facilitate reconstruction of theoutput layer. For example, a higher number of deconvolution layers makesit possible to reconstruct an output with greater precision.

As a variant, the CONV(64) convolution layers can have a depth differentfrom 64 in order to deal with a number of different local features. Forexample, a depth of 128 allows local processing of 128 differentfeatures in a complex hyperspectral scene.

Alternatively, the MaxUnpool(2,2) interpolation layers may be ofdifferent interpolation dimensions. For example, a MaxUnpool(4, 4) layerincreases the processing dimension of the top layer.

Alternatively, the ACT activation layers of the ReLu(x) type insertedfollowing each convolution and deconvolution, may be of different type.For example, the softplus function defined by the equation:ƒ(x)=log(1+e^(x)) can be used.

As a variant, the MaxPool(2,2) decimation layers can be of differentdecimation dimensions. For example, a MaxPool(4,4) layer makes itpossible to reduce the spatial dimension more quickly and to concentratethe semantic research of the neural network on local features.

As a variant, fully connected layers can be inserted between the twocentral convolution layers at line 6 of the description in order toprocess the detection in a higher mathematical space. For example, threefully connected layers of size 128 can be inserted.

Alternatively, the dimensions of the CONV(64) convolution, MaxPool(2, 2)decimation, and MaxUnpool(2, 2) interpolation layers can be adjusted onone or more layers, in order to adapt the architecture of the neuralnetwork closest to the type of features sought in the hyperspectralscene.

Alternatively, normalization layers, for example of the BatchNorm orGroupNorm type, as described in “Batch Normalization: Accelerating DeepNetwork Training by Reducing Internal Covariate Shift”, Sergey Ioffe,Christian Szegedy, February 2015 and “Group Normalization”, Yuxin Wu,Kaiming He, FAIR, June 2018, can be inserted before or after eachactivation layer or at different levels of the structure of the neuralnetwork.

The weights of said neural network 212 are calculated by means oflearning. For example, backward propagation of the gradient or itsderivatives from training data can be used to calculate these weights.

Alternatively, the neural network 212 can determine the probability ofthe presence of several distinct features within the same observedscene. In this case, the last convolutional layer will have a depthcorresponding to the number of distinct features to be detected. Thusthe convolutional layer CONV(1) is replaced by a convolutional layerCONV(u), where u corresponds to the number of distinct features to bedetected.

FIG. 12 illustrates a device 302 for capturing a hyperspectral scene 203comprising a set of sensors making it possible to obtain at least oneimage compressed in two dimensions 211 or 213 and at least one standardimage 312 of a hyperspectral focal plane 303 of an observed scene.

As illustrated in FIG. 13, the capture device 302 comprises at least oneacquisition device, or sensor, 301 of a compressed image as describedabove with reference to FIG. 8.

The capture device 302 can also comprise a device for acquiring anuncompressed “standard” image, comprising a converging lens 331 and acapture surface 232. The capture device 302 can also include a devicefor acquiring a compressed image as described above with reference toFIG. 9.

In the presented example, the standard image acquisition device and thecompressed image acquisition device are arranged juxtaposed withparallel optical axes, and optical beams at least partially overlapping.Thus, a portion of the hyperspectral scene is imaged at once by theacquisition devices. Thus, the focal planes of the various imageacquisition sensors are offset from each other transversely to theoptical axes of these sensors.

As a variant, a set of partially reflecting mirrors is used so as tocapture said at least one non-diffracted standard image 312 and said atleast one compressed image 211, 213 of the same hyperspectral scene 203on several sensors simultaneously.

Alternatively, the sensing surface 232 can be a device whose sensedwavelengths are not in the visible part. For example, the device 202 canintegrate sensors whose wavelength is between 0.001 nanometer and 10nanometers or a sensor whose wavelength is between 10,000 nanometers and20,000 nanometers, or a sensor whose wavelength is between 300nanometers and 2000 nanometers.

When the images 211, 312 or 213 of the observed hyperspectral focalplane are obtained, the detection means implement a neural network 214to detect a feature in the observed scene from the information of thecompressed images 211 and 213, and the standard image 312.

As a variant, only the compressed 211 and standard 312 images are usedand processed by the neural network 214.

As a variant, only the compressed 213 and standard 312 images are usedand processed by the neural network 214.

Thus, when the description relates to a set of compressed images, it isat least one compressed image.

This neural network 214 aims at determining the probability of thepresence of the sought feature for each pixel localised at the x and ycoordinates of the observed hyperspectral scene 203.

To do this, as illustrated in FIG. 14, the neural network 214 comprisesan encoder 251 for each compressed image and for each uncompressedimage; each encoder 251 has an input layer 250, capable of extractinginformation from the image 211, 312 or 213. The neural network mergesthe information coming from the various encoders 251 by means ofconvolution layers or fully connected layers 252 (special case shown inthe figure). A decoder 253 and its output layer 350, capable ofprocessing this information so as to generate an image whose intensityat each pixel, at the x and y coordinate, corresponds to the probabilityof the presence of the feature at the x and y coordinates of thehyperspectral scene 203, is inserted following the fusion of theinformation.

As illustrated in FIG. 11, the input layer 250 of an encoder 251 isfilled with the different diffractions of the compressed image 211 asdescribed above.

The above-described filling corresponds to the filling of the firstinput (“Input1”) of the neural network, according to the architecturepresented below.

For the second input (“Input2”) of the neural network, the population ofthe input layer relative to the “standard” image is populated bydirectly copying the “standard” image in the neuronal network.

According to an exemplary embodiment where a compressed image 213 isalso used, the third input “Input3” of the neural network is populatedas described above for the compressed image 213.

A neural network architecture allowing the direct detection of featuresin the hyperspectral scene can be as follows:

Input 1 Input 2 Input 3 ⇒ CONV (64) ⇒ CONV (64) ⇒ CONV (64) ⇒ MaxPool(2, 2) ⇒ MaxPool (2, 2) ⇒ MaxPool (2, 2) ⇒ CONV (64) ⇒ CONV (64) ⇒ CONV(64) ⇒ MaxPool (2, 2) ⇒ MaxPool (2, 2) ⇒ MaxPool (2, 2) ⇒ CONV (64) ⇒CONV (64) ⇒ MaxUnpool (2, 2) ⇒ CONV (64) ⇒ MaxUnpool (2, 2) ⇒ CONV (64)⇒ MaxUnpool (2, 2) ⇒ CONV (1) ⇒ Output

In this description, “Input1” corresponds to the portion of the inputlayer 250 populated from the compressed image 211. “Input2” correspondsto the portion of the input layer 250 populated from the standard image312, and “Input3” corresponds to the portion of the input layer 250populated from the compressed image 213. The line “CONV (64)” in thefifth line of the architecture operates information fusion.

As a variant, the line “CONV (64)” in the fifth line of the architectureoperating the information fusion can be replaced by a fully connectedlayer having as input all of the MaxPool(2, 2) outputs of the processingpaths for all of the inputs “input1”, “input2” and “input3” and asoutput a tensor of order one serving as input to the next layer “CONV(64)” presented in the sixth line of architecture.

In particular, the fusion layer of the neural network takes into accountthe shifts of the focal planes of the different image acquisitionsensors, and integrates the homographic function allowing theinformation from the different sensors to be merged taking into accountthe parallaxes of the different images.

The variants presented above for the embodiment of FIG. 11 can also beapplied here.

The weights of said neural network 214 are calculated by means oflearning. For example, backward propagation of the gradient or itsderivatives from training data can be used to calculate these weights.

Alternatively, the neural network 214 can determine the probability ofthe presence of several distinct features within the same observedscene. In this case, the last convolutional layer will have a depthcorresponding to the number of distinct features to be detected. Thusthe convolutional layer CONV (1) is replaced by a convolutional layerCONV (u), where u corresponds to the number of distinct features to bedetected.

According to an alternative embodiment, as shown in FIG. 11, a separatededicated acquisition device is not necessarily used to obtain the“standard” image 312. Indeed, as presented above in connection with FIG.9, in certain cases, part of the compressed image 211 comprises a“standard” image of the hyperspectral scene. These include the imageportion C described above. In this case, this portion of image “C” ofthe compressed image 211 can be used as the “standard” image for inputof the neural network.

Thus, the neural network 214 uses, for the direct detection of thesought features, the information of said at least one compressed imageas follows:

-   -   the light intensity in the central and non-diffracted part of        the focal plane of the scene at the x and y coordinates; and    -   light intensities in each of the diffractions of said compressed        image whose coordinates x ‘and y’ are dependent on the        coordinates x and y of the non-diffracted central part of the        focal plane of the scene.

The invention has been presented above in different variants, in which adetected feature of the hyperspectral scene is a two-dimensional imagewhose value of each pixel at the coordinates x and y corresponds to theprobability of presence of a feature at the same x and y coordinates ofthe hyperspectral focal plane of the scene 203. In particular, thefeature corresponds to a feature potentially indicative of the presenceof a weed or a leaf symptom of deficiency or disease in this pixel. Eachweed, each leaf symptom of deficiency or disease can be characterized byone or more features. The detection system then combines the results ofthe detection of each feature associated with a weed or a leaf symptomof deficiency or disease to determine a probability of the presence ofthe weed or the leaf symptom of deficiency or disease. If necessary,this process is repeated for all the predetermined weeds or foliarsymptoms of deficiency or disease sought in the field. One can, however,alternatively, provide, according to the embodiments of the invention,the detection of other features. According to an example, such anotherfeature can be obtained from the image from the neural network presentedabove. For this, the neural network 212, 214, can have a subsequentlayer, suitable for processing the image in question and determining thesought feature. According to an example, this subsequent layer can forexample count the pixels of the image in question for which theprobability is greater than a certain threshold. The result obtained isthen an area (possibly related to a standard area of the image).According to an example of application, if the image has, in each pixel,a probability of the presence of a chemical compound, the resultobtained can then correspond to a concentration of the chemical compoundin the hyperspectral image scene which can be indicative of a weed orfoliar symptom of deficiency or disease.

According to another example, this subsequent layer may for example haveonly one neuron, the value of which (real or boolean) will indicate thepresence or absence of an object or a particular feature sought in thehyperspectral scene. This neuron will have a maximum value in the eventof the presence of the object or the feature and a minimum value in theopposite case. This neuron will be fully connected to the previouslayer, and the connection weights will be calculated by means oflearning.

According to a variant, it will be understood that the neural networkcan also be architectured to determine this feature without goingthrough the determination of an image of probabilities of presence ofthe feature in each pixel.

In the context of this patent application, the detection systemdescribed above is considered to be a single detection system, even ifit uses different sensors whose information is merged to detect a weedor deficiency or disease leaf syndrome.

In addition, each detection system 2 can comprise a localisation system,of the type comprising an inertial unit and/or a geolocalisation system.

The agricultural treatment control device further comprises acommunication system connecting the deficiency or disease foliarsymptoms or weeds detection systems 2. The communication system isadapted to exchange data between the deficiency or disease foliarsymptoms or weeds detection systems 2 such as, in particular, data ofdetection of weeds or leaf symptoms of deficiencies or disease, data oflocalisation from inertial units, and/or geolocalisation systems.

The plurality of said at least one controllable agricultural treatmentdevice 3 is also fixed on the agricultural machine so as to be able totreat the target plants 4. As can be seen in particular in FIG. 1, theagricultural treatment devices 3 can be arranged spaced from each otherin a horizontal direction transverse to the direction of advance. Theycan for example be carried by a transverse beam of the agriculturalmachine, if necessary by the same beam which carries the detectionsystems 2. In addition, they can be spaced from these in the transversedirection. The agricultural treatment control device further comprises asystem for localising agricultural treatment devices. The agriculturaltreatment control device further comprises a communication systemconnecting the deficiency or disease foliar symptoms or weeds detectionsystems 2. The agricultural treatment device also comprises acommunication system suitable for exchanging data between the deficiencyor disease foliar symptoms or weeds detection systems 2 and theagricultural treatment devices 3.

The number of controllable agricultural treatment devices 3 need not bethe same as the number of deficiency or disease foliar symptoms or weedsdetection systems 2. In fact, according to one example, thecollaborative treatment decision is transmitted to the controllableagricultural treatment device 3 having the least distance from thetarget plant.

FIG. 15 illustrates the device, provided with two deficiency or diseasefoliar symptoms or weeds detection systems 2, mounted on an agriculturalmachine 1, in which each of the deficiency or disease foliar symptoms orweeds detection systems 2, is directed at an angle to the ground of theagricultural parcel 5, and having an overlap of their respectivedetection zones. In what follows, the first detection system will becharacterized by the reference “0.1”, and the second detection systemwill be characterized by the reference “0.2”.

At each instant, said deficiency or disease foliar symptoms or weedsdetection system 2.1 takes a photograph 6.1 of the area of agriculturalfield 5 facing its objective; said deficiency or disease foliar symptomsor weeds detection system 2.2, takes a picture 6.2 of the area of theagricultural field 5 facing its objective; said areas facing the opticalobjectives 9 of said deficiency or disease foliar symptoms or weedsdetection systems 2.1 and 2.2 have a common area of acquisition.

FIG. 16 gives an example of a collaborative processing method foracquired data. The collaborative processing method is designated by thereference 8, and the steps thereof by reference signs “.indicia”. Asillustrated in FIG. 16, capturing 8.1 of the image information of thetravelled agricultural field 5 makes it possible to obtain the acquiredimages 6.1 and 6.2.

Preferably, the plurality of said at least two deficiency or diseasefoliar symptoms or weeds detection systems 2 is composed of homogeneoussystems, having the same detection properties.

The images 6.1 and 6.2 acquired respectively by said deficiency ordisease foliar symptoms or weeds detection systems 2.1 and 2.2 areprocessed locally in each of said deficiency or disease foliar symptomsor weeds detection systems 2.1 and 2.2, in order to project each of saidimages acquired on the ground plane into an image projected on theground 7.1 and 7.2. The following discussion can be applied to eachdetection system 2.

The projection on the ground of said image data is calculated accordingto the following relationships:

Img_(projected) = R⁻¹.Img_(acquired) R = R_(z).R_(y).R_(x)$R_{x} = \begin{bmatrix}1 & 0 & 0 \\0 & {\cos\;\gamma} & {{- \sin}\;\gamma} \\0 & {\sin\;\gamma} & {\cos\;\gamma}\end{bmatrix}$ $R_{y} = \begin{bmatrix}{\cos\;\beta} & 0 & {\sin\;\beta} \\0 & 1 & 0 \\{{- \sin}\;\beta} & 0 & {\cos\;\beta}\end{bmatrix}$ $R_{z} = \begin{bmatrix}{\cos \propto} & {{- \sin}\; \propto} & 0 \\{\sin\; \propto} & {\cos\; \propto} & 0 \\0 & 0 & 1\end{bmatrix}$

Where:

-   -   lmg_(projected) is the tensor containing the pixels of the image        projected on the ground; and    -   lmg_(acquired) is the tensor containing the pixels of said raw        image data; and    -   R is the matrix containing the rotations along the three roll        axes, pitch and yaw; and    -   α is the yaw angle; and    -   β is the roll angle; and    -   γ is the pitch angle.

The angles α, β, and γ, correspond respectively to the current yaw, rolland pitch angles of the deficiency or disease foliar symptoms or weedsdetection system 2 considered as calculated from the raw data from theinertial unit on board the considered deficiency or disease foliarsymptoms or weeds detection system 2; this roll, pitch and yawinformation is calculated continuously and kept up to date by theconsidered deficiency or disease foliar symptoms or weeds detectionsystem 2 by means of an attitude estimation algorithm using the rawinformation of said inertial unit on board the considered deficiency ordisease foliar symptoms or weeds detection system 2. For example, theattitude estimation algorithm, used to calculate roll, pitch and yawinformation, can be an extended Kalman filter, a Mahony or Madgwickalgorithm. The document “A comparison of multisensor attitude estimationalgorithm”, A. Cirillo, P. Cirillo, G. De Maria, C. Natale, S. Pirozzi,in “Multisensor attitude estimation: Fundamental concepts andapplications, Chapter 29, Publisher: CRC Press, Editors: H. Fourati, DECBelkhiat, pp. 529-539, September 2016, describes and compares a set ofalgorithms for merging data from inertial units in order to extract theattitude, defined by the roll, pitch, and yaw angles of the system.

As illustrated in FIG. 16, the ortho-projection 8.2 of the imageinformation acquired from the travelled agricultural field 5 makes itpossible to obtain the acquired images 7.1 and 7.2 from images 6.1 and6.2.

Said image data projected on the ground are used to detect the presenceof weeds or leaf symptoms of deficiencies or diseases from the featuresspecific to weeds or leaf symptoms of deficiencies or diseasesdetermined by one of the methods above, in order to detect the zones,identified at the coordinates of the image X_(detect) and Y_(detect), insaid projected image data in which the target plants 4 are present. Atarget plant 4 is a plant for which the detection device detects a weedor a leaf symptom of deficiency or disease. As shown in FIG. 16, each ofthe detections 8.3 of the presence of weeds or leaf symptoms ofdeficiencies or diseases is supplemented with a probability of thepresence of said features specific to weeds or leaf symptoms ofdeficiencies or diseases. In some exemplary embodiments, thisprobability information is necessary for the geostatistical calculationsmaking it possible to decide on the application of a treatment on thetarget plant.

As illustrated in FIG. 16, each of said detections of weeds or foliarsymptoms of deficiencies or diseases is geolocalised 8.4 at lat and Ingcoordinates by means of the geolocalisation system on board in each ofsaid at least two deficiency or disease foliar symptoms or weedsdetection systems 2.

The calculation of geolocalisation 8.4 of a weed detection or foliarsymptom of deficiency or disease is based on the followingrelationships:

Distance=ratio_(pixel2meter)√[(X _(detect) −w _(img)/2)²+(Y _(detect) −h_(img)/2)²]

Bearing=cos[(Y _(detect) −h _(img)/2)/(distance/ratio_(pixel2meter))]

Rad_(fract)=distance/EARTH_(RADIUS)

$\begin{matrix}{{lat}_{1} = {{lat} \cdot \frac{\pi}{180}}} \\{{lng}_{1} = {{lng} \cdot \frac{\pi}{180}}}\end{matrix}$ $\begin{matrix}{{lat}_{21} = {\sin\mspace{11mu}{\left( {lat}_{1} \right) \cdot {\cos\left( {rad}_{fract} \right)}}}} \\{{lat}_{22} = {{co}\mspace{11mu}{{s\left( {lat}_{1} \right)} \cdot {si}}\mspace{11mu}{{n\left( {rad}_{fract} \right)} \cdot \cos}\mspace{11mu}({bearing})}}\end{matrix}$ $\begin{matrix}{{lng}_{21} = {{{\sin({bearing})} \cdot \sin}\mspace{11mu}{\left( {rad}_{fract} \right) \cdot \cos}\mspace{11mu}\left( {lat}_{1} \right)}} \\{{lng}_{22} = {{\cos\left( {rad}_{fract} \right)} - \left( {{{\sin\left( {lat}_{1} \right)} \cdot \sin}\mspace{11mu}\left( {lat}_{2} \right)} \right.}}\end{matrix}$Lat_(target)(180·asin(lat₂₁+lat₂₂))/π

Lng_(targ et)=(180·(lng₁+atan 2(lng₂₁,lng₂₂)+3π)mod 2π)−π))/π

Where:

-   -   EARTHRADIUS is the mean radius of the Earth, ie 6,371,000        meters; and    -   ratiopixel2meter is the ratio between a pixel of the image and a        meter on the ground; and    -   X_(detect) is the x coordinate, in pixels, of the detection        center in the image; and    -   Y_(detect) is the y coordinate, in pixels, of the center of        detection in the image; and    -   W_(img) is the width of the image in pixels; and    -   h_(ing) is the height of the image in pixels; and    -   Lat is the latitude measured by said geolocalisation system of        said deficiency or disease foliar symptoms or weeds detection        system 2; and    -   lng is the longitude measured by said geolocalisation system of        said deficiency or disease foliar symptoms or weeds detection        system 2; and    -   lat_(target) is the latitude of the target plant 4 detected in        the image; and    -   lng_(target) is the longitude of the target plant 4 detected in        the image.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems 2 continuously obtains the detection informationgeolocalised by the coordinates lat_(target) and Inn target by means ofthe communication system between the different deficiency or diseasefoliar symptoms or weeds detection systems 2, from all the otherdeficiency or disease foliar symptoms or weeds detection systems 2. Eachof said at least two deficiency or disease foliar symptoms or weedsdetection systems 2 thus continuously communicates detection informationgeolocalised by the coordinates lat_(target) and Ing_(target) target bymeans of the communication system between the different deficiency ordisease foliar symptoms or weeds detection systems 2 to all the otherdeficiency or disease foliar symptoms or weeds detection systems 2. Forexample, the GeoJSON format, as described in the document RFC7946, “TheGeoJSON Format”, IETF August 2016, makes it possible to transport saidgeolocalisation detection information on said communication system.

As a variant, the ESRI Shapefile format, as described in the documentESRI Shapefile technical description, June 1998, makes it possible totransport said geolocalised detection information on said communicationsystem.

As a variant, said latitude and longitude information can be calculatedfrom the raw information from the inertial units of all of said at leasttwo deficiency or disease foliar symptoms or weeds detection systems 2.Said raw information from the inertial units being exchanged by means ofthe communication system continuously connecting said at least twodeficiency or disease foliar symptoms or weeds detection systems 2, thelatitude estimation algorithm, executed on each of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2 canuse all of the raw information. Thus, the latitude and longitudeinformation is calculated relatively in the coordinate system of thetraveled agricultural field. For example, an extended Kalman filter canbe used in each of said at least two deficiency or disease foliarsymptoms or weeds detection systems, by taking data from the inertialunits of all of said at least two deficiency or disease foliar symptomsor weeds detection systems. In this variant, the calculation of thegeolocalisation 8.4 of a detection of weed or leaf symptom of deficiencyor disease is based on the same relationship with the followingelements:

-   -   Lat is the latitude of said deficiency or disease foliar        symptoms or weeds detection system 2 calculated in the        coordinate system of the travelled agricultural field from the        data coming from the inertial units of all of said at least two        deficiency or disease foliar symptoms or weeds detection        systems; and    -   lng is the longitude of said deficiency or disease foliar        symptoms or weeds detection system 2 calculated in the        coordinate system of the travelled agricultural field from the        data from the inertial units of all of said at least two        deficiency or disease foliar symptoms or weeds detection        systems.

As a variant, one does not necessarily use a geolocalisation of thedetections of weeds or foliar symptoms of deficiencies or diseases, butto a localisation of these in an instantaneous frame of reference of theagricultural machine. Such a localisation may be sufficient, insofar asthe processing can also be ordered in this frame of reference. Thiscould be the case in particular if the detection systems and theprocessing systems have known relative positions over time, for exampleare fixed with respect to each other over time. For a deficiency ordisease foliar symptoms or weeds detection system, the coordinates(x_(target); y_(target)) of the target relative to the center of thesensor can for example be determined as follows:

dist_(away)=tan(sensor_(angle))·sensor_(height)

X _(target)=ratiopixel2meter·(X _(detect) −w _(img)/2)

Y _(target)=dist_(away)+ratiopixel2meter·(Y _(detect) −h _(img)/2)

Where:

-   -   sensor_(angle) is the angle between the vertical and the average        viewing angle of the deficiency or disease foliar symptoms or        weeds detection system 2;    -   sensorheight is the height on the ground of the deficiency or        disease foliar symptoms or weeds detection system 2;    -   ratiopixel2meter is the ratio between a pixel in the image and a        meter on the ground;    -   X_(detect) is the x coordinate, in pixels, of the center of        detection in the image;    -   Y_(detect) is the y coordinate, in pixels, of the center of        detection in the image;    -   w_(img) is the width of the image in pixels;    -   him is the height of the image in pixels;    -   X_(target) is the relative longitudinal coordinate in meters of        the target plant 4 detected in the image;    -   Y_(target) is the relative coordinate in meters facing said        deficiency or disease foliar symptoms or weeds detection system        2 of the target plant 4 detected in the image.

All of the information on said detections of weeds or leaf symptoms ofdeficiencies or diseases from all of said at least two deficiency ordisease foliar symptoms or weeds detection systems 2 is stored in ageographic database local to each of said at least two deficiency ordisease foliar symptoms or weeds detection systems.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems 2 having its detection zone of the sought-afterfeatures; weeds or leaf symptoms of deficiencies or diseases; inagricultural field 5, overlapping with said at least two neighbordeficiency or disease foliar symptoms or weeds detection systems 2,lateral overlapping of said information for detection of weeds or foliarsymptoms of deficiencies or diseases is obtained.

Likewise, each of said at least two deficiency or disease foliarsymptoms or weeds detection systems 2 detecting at the present time thesought features of weeds or leaf symptoms of deficiencies or diseases inthe agricultural field 5 in the detection zone within reach of theoptical objective of said deficiency or disease foliar symptoms or weedsdetection system 2, a temporal recovery of said information ofdetections of weeds or leaf symptoms of deficiencies or disease isobtained. By temporal overlap, reference is made to the fact that thedetection zones in two successive distinct instants overlap if thefrequency of determination is sufficiently high. FIG. 17 illustratesthis embodiment, and represents in dotted lines the optical fieldacquired by the deficiency or disease foliar symptoms or weeds detectionsystem 2 at a first instant t1, and in dashed lines the optical fieldacquired by the same deficiency or disease foliar symptoms or weedsdetection system 2 at a second instant t2. The optical fields areshifted geographically due to the movement of the agricultural machineduring the time interval. The snapshot and image obtained at the secondinstant are represented with the index “0.3”. However, at all times,detections are geolocalised in a common frame of reference.

Thus, said information for detecting weeds or leaf symptoms ofdeficiencies or diseases stored in said geographic database local toeach of said at least two deficiency or disease foliar symptoms or weedsdetection systems 2 contains the redundancies of said information ofdetections of weeds or foliar symptoms of deficiencies or diseases.Operation 8.5 of the merger can be a krigeage operation, as described inthe book “Lognormal-de VVijsian Geostatistics for Ore Evaluation”, DGKrige, 1981, ISBN 978-0620030069, taking into account all of saidgeolocalised detection information of weeds or leaf symptoms ofdeficiencies or diseases and containing the probability of detectioninformation, coming from the plurality of said at least two deficiencyor disease foliar symptoms or weeds detection systems 2, as well as thelateral and temporal overlap information, thus confirming theprobabilities of detection of weeds or leaf symptoms of deficiencies ordiseases. Thus, at a given detection point, the result is determinedfrom the detection result obtained for this point by each of thedetection systems. The result makes it possible to decide whether or notto treat this point. For example, we compare the result with a certainpredetermined threshold and, if the result is positive, we order theapplication of the treatment.

The merger in question takes into account the quality of the detection.For example, when the merged detections include maps of the probabilityof the presence of a weed or a leaf symptom of deficiency or disease,the result of the fusion may include a map of the probability of thepresence of the weed or leaf symptom of deficiency or disease obtainedfrom these individual maps. Therefore, intrinsically, each individualmap carries information about the quality of the detection, and themerged result takes this quality into account. For example, if, at agiven location, a detection system determines a probability of thepresence of a leaf symptom of a certain disease at 90%, and anotherdetection system determines a probability of the presence of a leafsymptom of this same disease at 30%, the quality of detection of atleast one of the two detection systems is poor, and the final resulttranscribes this quality of detection.

According to a variant, during this fusion, the distance of eachdetection is also taken into account. Indeed, if at a given location,being close to the optical axis of a detection system, determines aprobability of the presence of a leaf symptom of a certain disease at30%, and another detection system, for which this same place is distantfrom the optical axis, determines a 90% probability of the presence of aleaf symptom of the same disease, we will apply a greater weight to thedetection system facing the studied localisation during fusion.

As a variant, operation 8.5 of fusion is an operation taking intoaccount all of the geolocalised information on the detection of weeds orleaf symptoms of deficiencies or diseases and containing the informationon the probability of detection, from the plurality of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2, aswell as the information on lateral and temporal overlaps, in order tocalculate the consolidated probabilities of geolocalised detections ofweeds or foliar symptoms deficiencies or diseases; Said consolidationoperation taking into account the probabilities of each geolocaliseddetection of weeds or leaf symptoms of deficiencies or diseases.

In the variant of FIG. 17, the localised detection information obtainedfor several spaced moments is merged as described above. This embodimentis, if applicable, applicable to a single deficiency or disease foliarsymptoms or weeds detection system. In this case, the collaborative workis done from two detections spaced in time from the same deficiency ordisease foliar symptoms or weeds detection system. If the agriculturaltreatment control device includes a single detection of weed or foliarsymptom of deficiency or disease, it does not implement a communicationsystem between deficiency or disease foliar symptoms or weeds detectionsystems. However, a communication system between the deficiency ordisease foliar symptoms or weeds detection system and the treatmentdevice remains necessary.

Each of said at least two deficiency or disease foliar symptoms or weedsdetection systems continuously calculates the instantaneous speed ofmovement by means of said localisation information obtained by means ofsaid localisation system. The speed information is necessary in order toestimate the order of time of said at least one agricultural processingdevice and to anticipate the processing time as a function of saidagricultural processing device.

Thus, depending on the nature and detected localisation of weeds or leafsymptoms of deficiencies or diseases, the nature and localisation of thetreatment devices, and the speed of movement, the control devicedetermines the processing device(s) to be actuated, and the temporalcharacteristics (instant, duration, etc.) of this actuation.

With regard to the calculation of the command 8.6 to be sent to said atleast one agricultural treatment device 3, each of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2estimates at each instant and for each of said target plants 4 currentlyin range of said at least one treatment device 3, which of said at leastone treatment device 3 is the most suitable for treating said targetplant 4.

The control commands are transmitted to said at least one agriculturaltreatment device by means of communication between said at least twodeficiency or disease foliar symptoms or weeds detection systems andsaid at least one agricultural treatment device.

With regard to controlling said at least one agricultural treatmentdevice, all of the information from said detections of weeds or leafsymptoms of deficiencies or diseases are geolocalised, agriculturaltreatment devices are also geolocalised, and said at least oneagricultural treatment device are actuated at the exact moment when saidat least one agricultural treatment device is above the target plants.

For example, when said at least one agricultural treatment device 3 is aspreading nozzle, the command 8.7 to be sent to each of said at leastone agricultural treatment device 3 is a pressure and flow controltaking into account the presence of a target plant at the instantpresent in the spraying zone of said spreading nozzle.

As a variant, when said at least one agricultural processing device 3 isa LASER, the command 8.7 to be sent to each of said at least oneagricultural processing device 3 is a command for transverse andlongitudinal shifts, and for lighting power taking into account thepresence of a target plant at the instant present in the range of saidLASER.

As a variant, when said at least one agricultural treatment device 3 isa high pressure water jet, the command 8.7 to be sent to each of said atleast one agricultural treatment device 3 is a pressure and flow controltaking into account the presence of a target plant at the instantpresent in the range area of the high pressure water injection nozzle.

As a variant, when said at least one agricultural treatment device 3 isa mechanical hoeing weeding tool, the command 8.7 to be sent to each ofsaid at least one agricultural treatment device 3 is an activationcommand taking into account the presence of a target plant at theinstant present in the area of said mechanical hoeing weedkiller.

As a variant, when said at least one agricultural treatment device 3 isan electric weed control tool, the command 8.7 to be sent to each ofsaid at least one agricultural treatment device 3 is an activationcommand taking into account the presence of a target plant at theinstant present in the area of said electric weeding tool.

In the presentation above, the acquired image is first projected in agiven frame of reference, then the detection of weed or foliar symptomof deficiency or disease is implemented for the projected image.Alternatively, one could plan to start by making an image of theprobability of the presence of a weed or foliar symptom of deficiency ordetection from the raw acquired image, then to project it in the givenframe of reference.

In the presentation above, the geolocalisation of each detection systemis carried out independently, and the geolocalisation detections aremerged so as to decide on the possible treatment. In variants, asdescribed below, the geolocalisation of each detection system can bedone collaboratively.

In a first variant, said attitude information can be calculated from theraw information from the inertial units of all of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2. Saidraw information from inertial units being exchanged by means of thecommunication system continuously connecting said at least twodeficiency or disease foliar symptoms or weeds detection systems 2, theattitude estimation algorithm executed on each of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2 canuse all of the raw information. Thus, the estimates of roll, pitch andyaw are consolidated by a set of similar, consistent and covariantmeasures. For example, an extended Kalman filter can be used in each ofsaid at least two deficiency or disease foliar symptoms or weedsdetection systems, by taking data from the inertial units of all of saidat least two deficiency or disease foliar symptoms or weeds detectionsystems. The document “Data Fusion Algorithms for Multiple InertialMeasurement Units”, Jared B. Bancroft and Gerard Lachapelle, Sensors(Basel), Jun. 29, 2011, 6771-6798 presents an alternative algorithm formerging raw data from a set of inertial units to determine attitudeinformation.

In a second variant, said attitude information can be calculated fromthe raw information of the inertial units to which the geolocalisationdata of all of said at least two deficiency or disease foliar symptomsor weeds detection systems 2 are added. Said raw information from theinertial units as well as the geolocalisation data being exchanged bymeans of the communication system connecting the said at least twodeficiency or disease foliar symptoms or weeds detection systems 2, theattitude estimation algorithm can use all of the raw information. Forexample, an extended Kalman filter can be used in each of said at leasttwo deficiency or disease foliar symptoms or weeds detection systems,taking the data from inertial units as well as the geolocalisation datafrom the set of said at least two deficiency or disease foliar symptomsor weeds detection systems 2. Furthermore, a method, as described in thedocument “Attitude estimation for accelerated vehicles using GPS/INSmeasurements”, Minh-Duc Hua, July 2010, Control Engineering PracticeVolume 18, Issue 7, July 2010, pages 723-732, allows a fusion ofinformation from a geolocalisation system and an inertial unit.

For example, said communication system between said at least twodeficiency or disease foliar symptoms or weeds detection systems 2 andsaid at least one agricultural treatment device 3 is a wired Ethernet 1Gigabit network per second thus allowing each of said at least twodeficiency or disease foliar symptoms or weeds detection systems 2 tocommunicate with the other deficiency or disease foliar symptoms orweeds detection systems 2 as well as with said at least one agriculturaltreatment device 3.

With regard to the mapping of the agricultural field 5 travelled by saidagricultural machine, each of said at least two deficiency or diseasefoliar symptoms or weeds detection systems 2 locally build a mapping ofthe specific features; or the presence of weeds or leaf symptoms ofdeficiencies or diseases; using a local geographic database. Thegeolocalised detection information of the presence of weeds or leafsymptoms of deficiency or diseases, detected by all of said at least twodeficiency or disease foliar symptoms or weeds detection systems andexchanged by means of the system of communication, are thus stored ineach of said at least two deficiency or disease foliar symptoms or weedsdetection systems 2.

Thus, the content of each of said geographic databases locally stored ineach of said at least two deficiency or disease foliar symptoms or weedsdetection systems 2, represents the real state, as measured by all ofsaid at least two deficiency or disease foliar symptoms or weedsdetection systems 2, and sanitary state of said traveled agriculturalfield 5.

As a variant, the mapping information of the agricultural field 5travelled by said agricultural machine, is transmitted by means of acommunication system, and displayed on a control screen intended for thetechnician carrying out the processing of the agricultural field 5.

Preferably, the communication system used to transmit the mappinginformation of the agricultural field 5 to said control screen intendedfor the technician carrying out the treatment of the agricultural field5, comprises a wired Gigabit Ethernet network.

Alternatively, the communication system used to transmit the mappinginformation of the agricultural field 5 to said control screen intendedfor the technician processing the agricultural field 5, is a wired CANnetwork (“Control Area Network”).

The cartography of agricultural field 5 finds an advantageous use inorder to produce statistics of sprays or treatments applied to saidagricultural field 5. Said statistics also make it possible to measurethe prevalence, the presence and the quantity of certain species ofweeds, as well as their densities and stages. The prevalence, presenceand density of leaf symptoms of deficiencies or diseases can also becalculated from the information contained in the mapping of theagricultural field 5.

In the example presented, each detection system communicates withneighboring detection systems, for decision making for collaborativeprocessing. As a variant, it is possible to provide a central processorsuitable for communicating, via the communication system, with thedetection systems, making a decision, and communicating the processinginstructions to the processing devices 3 via the communication system.

According to the invention, it is sufficient for a single deficiency ordisease foliar symptoms or weeds detection system 2 to make acollaborative decision using information relating to other deficiency ordisease foliar symptoms or weeds detection systems.

The methods which are described can be computerized methods. They canthen be defined in computer programs, which can be executed by one ormore processors of programmable machines.

REFERENCES

-   agricultural machine 1-   deficiency or disease leaf symptoms or weeds detection systems 2-   detection systems 2.1 and 2.2-   agricultural treatment device 3-   target plant 4-   agricultural field 5-   photo 6.1, 6.2-   acquired images 7.1 and 7.2 from images 6.1 and 6.2-   capture 8.1-   ortho-projection 8.2-   detections 8.3-   geolocalisation calculation 8.4-   merger operation 8.5-   command 8.6-   command 8.7-   optical lens 9-   capture device 10-   first sensor 11-   second sensor 12-   third sensor 13-   diffracted image 14, 14′-   hyperspectral image 15-   building module 16-   non-diffracted image 17′-   infrared image 18′-   neural network 20-   characterization module 21-   isolate 25-   extract 26-   first converging lens 30-   opening 31-   collimator 32-   diffraction grating 33-   second converging lens 34-   capture area 35-   input layer 40-   output layer 41-   capture device 202-   Hyperspectral scene 203-   sensor, or acquisition system 204,-   two-dimensional compressed image 211-   neural network 212-   compressed image 213-   neural network 214-   input layer 230-   output layer 231-   sensing surface 232-   first converging lens 241-   mask 242-   collimator 243-   prism 244-   second converging lens 245-   capture surface 246-   entry layer 250-   encoder 251-   convolutional layers or fully connected layers 252-   decoder 253-   acquisition device, or sensor, 301-   capture device 302-   focal plane 303-   standard image 312-   converging lens 331-   output layer 350

1. Agricultural treatment control device to be mounted on anagricultural machine, said agricultural machine comprising at least onecontrollable treatment device, wherein the agricultural treatmentcontrol device comprises: at least one deficiency or disease foliarsymptoms or weeds detection system, each being adapted for attachment tothe agricultural machine; a localization system of at least onedeficiency or disease foliar symptoms or weeds detection system; atleast one deficiency or disease foliar symptoms or weeds detectionsystem being characterized in that it is adapted to collaborate with adeficiency or disease foliar symptoms or weeds detection system whosedetection zone partially overlaps with that of said deficiency ordisease foliar symptoms or weeds detection system in order tocollaboratively decide on the treatment to be applied to the detectionzone of said deficiency or disease foliar symptoms or weeds detection;and a communication system between said at least one deficiency ordisease foliar symptoms or weeds detection systems and at least onetreatment device.
 2. Device according to claim 1, wherein said at leastone deficiency or disease foliar symptoms or weeds detection system isadapted to collaborate with another deficiency or disease foliarsymptoms or weeds detection system whose detection zone partiallylaterally overlaps with that of said deficiency or disease foliarsymptoms or weeds detection system.
 3. Device according to claim 1,wherein said at least one deficiency or disease foliar symptoms or weedsdetection system is adapted to collaborate with a deficiency or diseasefoliar symptoms or weeds detection system whose detection zonetemporally overlaps with that of said deficiency or disease foliarsymptoms or weeds detection system.
 4. Device according to claim 1,wherein all of said at least one deficiency or disease foliar symptomsor weeds detection systems are adapted to collaboratively build amapping of the agricultural field travelled by said agriculturalmachine, said cartography being constructed by a geostatistical processwith localized detection data representing the real state as measured bysaid at least one deficiency or disease foliar symptoms or weedsdetection system.
 5. Device according to claim 4, further comprising acontrol screen, and in which the map of the travelled agricultural fieldis displayed on the control screen intended for the technician carryingout the treatment of the agricultural field.
 6. Device according toclaim 1, in which the localization system comprises a geolocalizationsystem and/or an inertial unit.
 7. Device according to claim 1, whichfurther comprises at least one of the following features: at least twodeficiency or disease foliar symptoms or weeds detection systems; atleast one deficiency or disease foliar symptoms or weeds detectionsystem is equipped with a localization system; at least one deficiencyor disease foliar symptoms or weeds detection system is adapted tocollaborate with another, deficiency or disease foliar symptoms or weedsdetection systems; at least one deficiency or disease foliar symptoms orweeds detection system comprises a hyperspectral sensor; a deficiency ordisease foliar symptoms or weeds detection system is adapted to detectthe presence of weeds or foliar symptoms of deficiencies or diseasesfrom peculiarities specific to weeds or foliar symptoms of deficienciesor diseases; a deficiency or disease foliar symptoms or weeds detectionsystem is adapted to detect an area for a weed or foliar symptom ofdeficiency or disease; a deficiency or disease foliar symptoms or weedsdetection system is supplemented with a probability of the presence ofsaid peculiarities specific to weeds or foliar symptoms of deficienciesor diseases; the localization system is adapted to localize thetreatment to be applied to the detection area; a communication systembetween said deficiency or disease foliar symptoms or weeds detectionsystems; a temporal overlap of said information on the deficiency ordisease foliar symptoms or weeds detection is obtained.
 8. Deviceaccording to claim 1, in which one detection system comprises a systemfor direct detection of features in the hyperspectral scene integratinga deep and convolutional neural network designed to detect at least onecharacteristic sought in said hyperspectral scene for a weed or a leafsymptom of deficiency or disease from at least one compressed image ofthe hyperspectral scene.
 9. Device according to claim 1, in which onedetection system comprises a system for detecting features in thehyperspectral scene comprising: a neural network configured to calculatea hyperspectral hypercube of the hyperspectral scene from at least onecompressed image and an uncompressed image of the hyperspectral scene, acharacterization module to detect the weed or the leaf symptom ofdeficiency or disease from the hyperspectral hypercube.
 10. A systemcomprising a device according to claim 4, and further comprising aprocessor adapted to produce statistics on spraying, prevalence,species, densities, or stages of weeds or foliar symptoms ofdeficiencies or diseases present in the agricultural field using themapping of the travelled agricultural field.
 11. System comprising adevice according to claim 1 and a controllable agricultural treatmentdevice of an agricultural machine, in which said agricultural treatmentdevice comprises at least one spray nozzle, the flow or pressure of saidat least one spray nozzle being controlled by the collaborative decisionof all of said at least two deficiency or disease foliar symptoms orweeds detection systems.
 12. System comprising a device according toclaim and a controllable agricultural treatment device of anagricultural machine, in which said device agricultural treatmentcomprises at least one LASER for destroying weeds, said at least oneLASER being controlled by the collaborative decision of all of said atleast two deficiency or disease foliar symptoms or weeds systems. 13.System comprising a device according to claim 1 and a controllableagricultural treatment device of an agricultural machine, in which saidagricultural treatment device comprises at least one high pressure waterjet whose objective is the destruction of weeds, said at least one highpressure water jet being controlled by the collaborative decision of allof said at least two deficiency or disease foliar symptoms or weedsdetection systems.
 14. System comprising a device according to claim 1and a controllable agricultural treatment device of an agriculturalmachine, in which said agricultural treatment device comprises at leastone mechanical hoeing weed control tool, said at least one mechanicalhoeing weed control tool being controlled by the collaborative decisionof all of said at least two deficiency or disease foliar symptoms orweeds detection systems.
 15. System comprising a device according toclaim 1 and a controllable agricultural treatment device of anagricultural machine, in which said agricultural treatment devicecomprises at least one electric weed control tool for destroying weeds,said at least one electric weed control tool being controlled by thecollaborative decision of all of said at least two deficiency or diseasefoliar symptoms or weeds detection systems.
 16. System according toclaim 10, in which the agricultural treatment device is localized. 17.Method for collaborative control of agricultural treatment to be mountedon an agricultural machine, said agricultural machine comprising atleast one controllable treatment device, wherein the agriculturaltreatment control method comprises: a collaborative decision of said atleast one deficiency or disease foliar symptoms or weeds detectionsystem of which the detection zones partially overlap, each beingsuitable for attachment to the agricultural machine and the localizationof the treatment to be applied to the detection area; and acommunication between said deficiency or disease foliar symptoms orweeds detection systems with said at least one treatment device. 18.Collaborative piloting method according to claim 17, the methodcomprising for each of at least two deficiency or disease foliarsymptoms or weeds detection systems, the steps of: Acquisition of a newimage datum from the ground of the travelled agricultural field on whichan agricultural machine moves by means of said deficiency or diseasefoliar symptoms or weeds detection system; and Acquisition of additionalposition information from said deficiency or disease foliar symptoms orweeds detection system by means of the localization system; andProjection of said image data acquired by each of said deficiency ordisease foliar symptoms or weeds detection systems on the ground plane;and Detection of the presence of weeds or foliar symptoms ofdeficiencies or diseases from said image data acquired and projectedonto said ground plane; and Calculation of the positions of weeds orleaf symptoms of deficiencies or diseases in the detection zone of saiddeficiency or disease foliar symptoms or weeds detection system; saidposition calculation using the localization information of saidlocalization system of said deficiency or disease foliar symptoms orweeds detection system and the detection information in said image data;and Communication of said positions of weeds or leaf symptoms ofdeficiencies or diseases in the detection zone of said deficiency ordisease foliar symptoms or weeds detection system to all of the otherdeficiency or disease foliar symptoms or weeds detection systems; andReception of said positions of weeds or foliar symptoms of deficienciesor diseases in the detection area of said deficiency or disease foliarsymptoms or weeds detector from other deficiency or disease foliarsymptoms or weeds detection systems; and Fusion of said positions ofweeds or foliar symptoms of deficiencies or diseases of all thedeficiency or disease foliar symptoms or weeds detection systems; andCalculation of the command to be sent to the treatment device concernedby the detection zone of said deficiency or disease foliar symptoms orweeds detection system; and Issuance of the command to the treatmentdevice concerned by the detection zone of said deficiency or diseasefoliar symptoms or weeds detection system.
 19. Collaborative pilotingmethod according to claim 18, further comprising at least one of thefollowing features: said projection uses information from said inertialunit of said deficiency or disease foliar symptoms or weeds detectionsystem in order to determine the angle of capture of the image datarelative to the normal vector on the ground; Communication of saidpositions of weeds or foliar symptoms of deficiencies or diseases in thedetection zone of said deficiency or disease foliar symptoms or weedsdetection system to others, in particular to all the other deficiency ordisease foliar symptoms or weeds detection systems; the fusion isweighted according to the quality and the calculated distance of eachdetection.
 20. Computer program comprising instructions which, when theprogram is executed by a computer, lead the latter to implement themethod according to claim 17.